{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MhoQ0WE77laV"
   },
   "source": [
    "# Train a Neural Network Model to Classify Images\n",
    "\n",
    "\n",
    "\n",
    "## Learning Objectives\n",
    "\n",
    "1. Undersand how to read and display image data\n",
    "2. Pre-process image data\n",
    "3. Build, compile, and train a neural network model\n",
    "4. Make and verify predictions\n",
    "\n",
    "\n",
    "\n",
    "## Introduction \n",
    "This lab trains a neural network model to classify images of clothing, such as  sneakers and shirts. You will learn how to read and display image data, pre-process image data, build, compile, and train a neural network model, and make and verify predictions\n",
    "\n",
    "Each learning objective will correspond to a __#TODO__ in the [student lab notebook](training-data-analyst/courses/machine_learning/deepdive2/image_classification/labs/5_fashion_mnist_class.ipynb) -- try to complete that notebook first before reviewing this solution notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dzLKpmZICaWN"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.6.0\n"
     ]
    }
   ],
   "source": [
    "# TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yR0EdgrLCaWR"
   },
   "source": [
    "## Import the Fashion MNIST dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DLdCchMdCaWQ"
   },
   "source": [
    "This lab uses the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here:\n",
    "\n",
    "<table>\n",
    "  <tr><td>\n",
    "    <img src=\"https://tensorflow.org/images/fashion-mnist-sprite.png\"\n",
    "         alt=\"Fashion MNIST sprite\"  width=\"600\">\n",
    "  </td></tr>\n",
    "  <tr><td align=\"center\">\n",
    "    <b>Figure 1.</b> <a href=\"https://github.com/zalandoresearch/fashion-mnist\">Fashion-MNIST samples</a> (by Zalando, MIT License).<br/>&nbsp;\n",
    "  </td></tr>\n",
    "</table>\n",
    "\n",
    "Fashion MNIST is intended as a drop-in replacement for the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset—often used as the \"Hello, World\" of machine learning programs for computer vision. The MNIST dataset contains images of handwritten digits (0, 1, 2, etc.) in a format identical to that of the articles of clothing you'll use here.\n",
    "\n",
    "This guide uses Fashion MNIST for variety, and because it's a slightly more challenging problem than regular MNIST. Both datasets are relatively small and are used to verify that an algorithm works as expected. They're good starting points to test and debug code.\n",
    "\n",
    "Here, 60,000 images are used to train the network and 10,000 images to evaluate how accurately the network learned to classify images. You can access the Fashion MNIST directly from TensorFlow. Import and load the Fashion MNIST data directly from TensorFlow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7MqDQO0KCaWS"
   },
   "outputs": [],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t9FDsUlxCaWW"
   },
   "source": [
    "Loading the dataset returns four NumPy arrays:\n",
    "\n",
    "* The `train_images` and `train_labels` arrays are the *training set*—the data the model uses to learn.\n",
    "* The model is tested against the *test set*, the `test_images`, and `test_labels` arrays.\n",
    "\n",
    "The images are 28x28 NumPy arrays, with pixel values ranging from 0 to 255. The *labels* are an array of integers, ranging from 0 to 9. These correspond to the *class* of clothing the image represents:\n",
    "\n",
    "<table>\n",
    "  <tr>\n",
    "    <th>Label</th>\n",
    "    <th>Class</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>0</td>\n",
    "    <td>T-shirt/top</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>1</td>\n",
    "    <td>Trouser</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>2</td>\n",
    "    <td>Pullover</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>3</td>\n",
    "    <td>Dress</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>4</td>\n",
    "    <td>Coat</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>5</td>\n",
    "    <td>Sandal</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>6</td>\n",
    "    <td>Shirt</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>7</td>\n",
    "    <td>Sneaker</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>8</td>\n",
    "    <td>Bag</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>9</td>\n",
    "    <td>Ankle boot</td>\n",
    "  </tr>\n",
    "</table>\n",
    "\n",
    "Each image is mapped to a single label. Since the *class names* are not included with the dataset, store them here to use later when plotting the images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IjnLH5S2CaWx"
   },
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Brm0b_KACaWX"
   },
   "source": [
    "## Explore the data\n",
    "\n",
    "Let's explore the format of the dataset before training the model. The following shows there are 60,000 images in the training set, with each image represented as 28 x 28 pixels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zW5k_xz1CaWX"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cIAcvQqMCaWf"
   },
   "source": [
    "Likewise, there are 60,000 labels in the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TRFYHB2mCaWb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YSlYxFuRCaWk"
   },
   "source": [
    "Each label is an integer between 0 and 9:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XKnCTHz4CaWg"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TMPI88iZpO2T"
   },
   "source": [
    "There are 10,000 images in the test set. Again, each image is represented as 28 x 28 pixels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2KFnYlcwCaWl"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rd0A0Iu0CaWq"
   },
   "source": [
    "And the test set contains 10,000 images labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iJmPr5-ACaWn"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ES6uQoLKCaWr"
   },
   "source": [
    "## Preprocess the data\n",
    "\n",
    "The data must be preprocessed before training the network. If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "m4VEw8Ud9Quh"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Wz7l27Lz9S1P"
   },
   "source": [
    "Scale these values to a range of 0 to 1 before feeding them to the neural network model. To do so, divide the values by 255. It's important that the *training set* and the *testing set* be preprocessed in the same way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bW5WzIPlCaWv"
   },
   "outputs": [],
   "source": [
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ee638AlnCaWz"
   },
   "source": [
    "To verify that the data is in the correct format and that you're ready to build and train the network, let's display the first 25 images from the *training set* and display the class name below each image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oZTImqg_CaW1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "59veuiEZCaW4"
   },
   "source": [
    "## Build the model\n",
    "\n",
    "Building the neural network requires configuring the layers of the model, then compiling the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Gxg1XGm0eOBy"
   },
   "source": [
    "### Set up the layers\n",
    "\n",
    "The basic building block of a neural network is the *layer*. Layers extract representations from the data fed into them. Hopefully, these representations are meaningful for the problem at hand.\n",
    "\n",
    "Most of deep learning consists of chaining together simple layers. Most layers, such as `tf.keras.layers.Dense`, have parameters that are learned during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9ODch-OFCaW4"
   },
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(128, activation='relu'),\n",
    "    keras.layers.Dense(10)\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gut8A_7rCaW6"
   },
   "source": [
    "The first layer in this network, `tf.keras.layers.Flatten`, transforms the format of the images from a two-dimensional array (of 28 by 28 pixels) to a one-dimensional array (of 28 * 28 = 784 pixels). Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn; it only reformats the data.\n",
    "\n",
    "After the pixels are flattened, the network consists of a sequence of two `tf.keras.layers.Dense` layers. These are densely connected, or fully connected, neural layers. The first `Dense` layer has 128 nodes (or neurons). The second (and last) layer returns a logits array with length of 10. Each node contains a score that indicates the current image belongs to one of the 10 classes.\n",
    "\n",
    "### Compile the model\n",
    "\n",
    "Before the model is ready for training, it needs a few more settings. These are added during the model's *compile* step:\n",
    "\n",
    "* *Loss function* —This measures how accurate the model is during training. You want to minimize this function to \"steer\" the model in the right direction.\n",
    "* *Optimizer* —This is how the model is updated based on the data it sees and its loss function.\n",
    "* *Metrics* —Used to monitor the training and testing steps. The following example uses *accuracy*, the fraction of the images that are correctly classified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Lhan11blCaW7"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qKF6uW-BCaW-"
   },
   "source": [
    "## Train the model\n",
    "\n",
    "Training the neural network model requires the following steps:\n",
    "\n",
    "1. Feed the training data to the model. In this example, the training data is in the `train_images` and `train_labels` arrays.\n",
    "2. The model learns to associate images and labels.\n",
    "3. You ask the model to make predictions about a test set—in this example, the `test_images` array.\n",
    "4. Verify that the predictions match the labels from the `test_labels` array.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Z4P4zIV7E28Z"
   },
   "source": [
    "### Feed the model\n",
    "\n",
    "To start training,  call the `model.fit` method—so called because it \"fits\" the model to the training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xvwvpA64CaW_"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 4s 70us/sample - loss: 0.5068 - accuracy: 0.8225\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 4s 67us/sample - loss: 0.3776 - accuracy: 0.8626\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 4s 68us/sample - loss: 0.3393 - accuracy: 0.8760\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 4s 68us/sample - loss: 0.3130 - accuracy: 0.8846\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 4s 66us/sample - loss: 0.2958 - accuracy: 0.8906\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 4s 67us/sample - loss: 0.2842 - accuracy: 0.8946\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 4s 68us/sample - loss: 0.2703 - accuracy: 0.9007\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 4s 69us/sample - loss: 0.2594 - accuracy: 0.9039\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 4s 67us/sample - loss: 0.2500 - accuracy: 0.9066\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 4s 67us/sample - loss: 0.2408 - accuracy: 0.9098\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f41d051b630>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "W3ZVOhugCaXA"
   },
   "source": [
    "As the model trains, the loss and accuracy metrics are displayed. This model reaches an accuracy of about 0.91 (or 91%) on the training data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wCpr6DGyE28h"
   },
   "source": [
    "### Evaluate accuracy\n",
    "\n",
    "Next, compare how the model performs on the test dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VflXLEeECaXC"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 - 0s - loss: 0.3671 - accuracy: 0.8758\n",
      "\n",
      "Test accuracy: 0.8758\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yWfgsmVXCaXG"
   },
   "source": [
    "It turns out that the accuracy on the test dataset is a little less than the accuracy on the training dataset. This gap between training accuracy and test accuracy represents *overfitting*. Overfitting happens when a machine learning model performs worse on new, previously unseen inputs than it does on the training data. An overfitted model \"memorizes\" the noise and details in the training dataset to a point where it negatively impacts the performance of the model on the new data. For more information, see the following:\n",
    "*   [Demonstrate overfitting](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit#demonstrate_overfitting)\n",
    "*   [Strategies to prevent overfitting](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit#strategies_to_prevent_overfitting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "v-PyD1SYE28q"
   },
   "source": [
    "### Make predictions\n",
    "\n",
    "With the model trained, you can use it to make predictions about some images.\n",
    "The model's linear outputs, [logits](https://developers.google.com/machine-learning/glossary#logits). Attach a softmax layer to convert the logits to probabilities, which are easier to interpret. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DnfNA0CrQLSD"
   },
   "outputs": [],
   "source": [
    "probability_model = tf.keras.Sequential([model, \n",
    "                                         tf.keras.layers.Softmax()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Gl91RPhdCaXI"
   },
   "outputs": [],
   "source": [
    "predictions = probability_model.predict(test_images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "x9Kk1voUCaXJ"
   },
   "source": [
    "Here, the model has predicted the label for each image in the testing set. Let's take a look at the first prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3DmJEUinCaXK"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.4933645e-08, 3.8640968e-13, 4.0086920e-10, 1.3744024e-10,\n",
       "       3.4285610e-10, 1.1337308e-05, 3.6800021e-09, 1.2348609e-03,\n",
       "       7.7709267e-10, 9.9875379e-01], dtype=float32)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-hw1hgeSCaXN"
   },
   "source": [
    "A prediction is an array of 10 numbers. They represent the model's \"confidence\" that the image corresponds to each of the 10 different articles of clothing. You can see which label has the highest confidence value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qsqenuPnCaXO"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "E51yS7iCCaXO"
   },
   "source": [
    "So, the model is most confident that this image is an ankle boot, or `class_names[9]`. Examining the test label shows that this classification is correct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Sd7Pgsu6CaXP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ygh2yYC972ne"
   },
   "source": [
    "Graph this to look at the full set of 10 class predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DvYmmrpIy6Y1"
   },
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'blue'\n",
    "  else:\n",
    "    color = 'red'\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  predictions_array, true_label = predictions_array, true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks(range(10))\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Zh9yABaME29S"
   },
   "source": [
    "### Verify predictions\n",
    "\n",
    "With the model trained, you can use it to make predictions about some images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "d4Ov9OFDMmOD"
   },
   "source": [
    "Let's look at the 0th image, predictions, and prediction array. Correct prediction labels are blue and incorrect prediction labels are red. The number gives the percentage (out of 100) for the predicted label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HV5jw-5HwSmO"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ko-uzOufSCSe"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 12\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kgdvGD52CaXR"
   },
   "source": [
    "Let's plot several images with their predictions. Note that the model can be wrong even when very confident."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hQlnbqaw2Qu_"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XxyqFa9r4ZSl/LSo/3gGoHRMnTszK8bp5cX73zJkzm+WY8dqceccE2hOeVAEAAABAAdxUAQAAAEABVR33tWHDBj300ENZ/aabbsrKY8eODbaNpxr3h/HFQ2P8qT7joXfxUBh/P/6QOKn+EJuNGzeW3U88vM6f1jjuQzzM0J8+/qWXXgra/D41NN25P4wwnma+S5cuZbf1p1zt1KlT7jGAfZG/LELe8Lk4ZsydOzeo++dXcy5f4O8rnlI9rucNX5w/f35WHjx4cNAWxy3/szBNMlCbTj755Kz8q1/9KmiLp0WfMWNGk44Rx728oc15Q5eBto5/3QAAAABQADdVAAAAAFAAN1UAAAAAUEBVc6q6desWTPc5derUrPziiy8G206ePLnsfuI8ID9Pqm/fvkFbXO/Vq1dWjnOq4ryp1atXZ+V58+YFbXFOwYYNG7JynNMwa9asoH700Udn5eHDhwdtDz/8cFaOpynNG5sc500ccMABQb1nz55Z2c8Va848D6C98M+nvHPEn3pdktasWRPU6+rqsnKcC9oYcUzJE+eA5eU73HfffVk5jkXPP/98UPfjz9q1ayvuD4DW8853vjMrx7nVcWzw86sbw7+ekOpfO/mKxD2g1vGkCgAAAAAK4KYKAAAAAAqo6vC/Dh06qHfv3ln9uuuuK7vtpk2bgvozzzyTleOheE8//XRWXrRoUdD2wgsvBHV/6vH4kXU8xMYf7hIPIzzqqKOC+hlnnJGVzznnnKAtfgSf57zzzsvKr7/+etDWr1+/oO4/gveHQEr1hwP6q5qPGTOmSX0D9hX+ub9t27ay28VTqMdDdv3zLh4qGA/FyRtCE7f59YaGBuYNv/HjpT8sWZImTZoU1P3jxJ8FQG0YNmxYVo6H6cXxyY9tCxYsCNpGjBhR9hhxCkZePCDFAO0ZT6oAAAAAoABuqgAAAACgAG6qAAAAAKCAquZUNUb37t2D+umnn16yLElXXXVVq/SpNdx///2tery8adqBfZWfC5WXkxRPLR7nX/n7iXOoYv65GJ+Xcd6UX4/b8vKv/CUlJGnKlClZ2c+1LMU/ztatW3O3BVB9cQ5VvLyCv6xMY3KqhgwZEtTjXPY+ffpkZXKq0J5xBQ0AAAAABXBTBQAAAAAFcFMFAAAAAAW0mZwqAKgWfx2Wurq6oM1fQ++zn/1s0PaXv/wlqPu5R43JX8zLoZLy17SKc8D8465fvz5oO/XUU7PyueeeG7R9/etfD+p+TlicqwGgOvLW27zggguCtttuuy2o+7Fi8uTJQZu/9mYsjol5ffLzq4D2hidVAAAAAFAAN1UAAAAAUADD/wCgAZs3b87K8VTo/tDAHTt2BG0DBgwI6q+++mpWjqcozpuqvTHyhv9IYX/jKeAHDhyYlfv37597HH8Y4eLFixvdTwDNL+/8P//884O2W265Jah37tw5K999991B29e+9rWyx4ynSc8bruwvKwG0NzypAgAAAIACuKkCAAAAgAK4qQIAAACAAsipAoAGnHTSSVl5ypQpQVuXLl2y8pgxY4K2V155pWU71oIWLFgQ1Hv06BHU/WnUJ06c2Cp9ApAvbwmFs88+O2iLpzf3z+nGLPlw5JFHBvUXX3wxqPsxcunSpRXvF2hreFIFAAAAAAVwUwUAAAAABXBTBQAAAAAFkFMFAA3wc4a2bt0atPlruzQmD6HWxWtu+fkWkrR9+/as3K1bt1bpE4B88Tp6eYYNGxbUp06dmpW3bNkStD399NNB/Z3vfGdWjtep2rZtW1D3Y8WqVasq7h/Q1rSfKwAAAAAAqAJuqgAAAACgAIb/AUADhg4dmpXHjx8ftPnTBTc0DG7nzp1ZOR6m45wr0sUmiY/p92nUqFFB2/vf//6gvm7duqx84okntkDvADSWmVW87RVXXBHUx44dm5UvueSSoM0f7he77LLLgvr69euDevfu3bPyu9/97or7B7Q1PKkCAAAAgAK4qQIAAACAAripAgAAAIACqppT9dxzz60ys8XV7APqGdbwJkDb0xrxxs8d2Jc89thjRd5OzEG71JavcX7961+3yH5vvfXWFtlvIxBv0GKqelPlnBtQzeMD2HcQbwC0JmIOsG9h+B8AAAAAFMBNFQAAAAAUYNVYGwUowsxWSsobp95f0qoKdlXJdq29r7ba92EMdUF7VEG8kWrvfKzG8ZpzX5VsQ8xBu1SD1zjtPZZUsl1l8cY51+Cf5D4oOSe5sRVuv0hy/Uu8vqmS9zd1+5z9XC65A8q0/a3k5khut+QmRG1fkNx8yc2T3Pu8189KX5svuc97r98quRck9y3vtS9L7oM5fRsvuZui134nuakVfrZTJfeHMp/5J438jirePmc/vSV3lVcfILk/N8f/j5X3QdOba7vW3ldb7ns1/yS3S3IzJTdbcndJrq6B7X8tuYvS8l/jc7+F+1ozMSc971em391Lkruigv5vSv93uORmt/B31erxo2n9rL3zsS3HklqPN0kfXb/0vJkpuWWSe9Ord652/7x+fltySyS3Lnq9i+QmpTFliuQO9tq+nL4+V3JnpK8NktxTaYz9gLft7yU3OOf4n5Pc30nuBi/ObPW+qwtq4Dv6seTeWe1+VN7f2jwfa3FfzXm8Sv4qHf53qaTJ6f+2RZdLOqBM22xJF0p6wn/RTIdLukTSEZLOkvQzM3UwUwdJP5V0tqTDJV1qpsPNdLSkrc7paEnvMFMvMw2RdLxz+l1O374o6UfecXtLOk5SLzONaPxHrbrekq7aU3FOKyUtNdNJ1esS9gFbndM453SkpO2S/qnaHdojjRm+moo5kn7rnMZJOlXSt8w0qKmftTmZqSPxA7XKOa1OY844STdI+u89dee0XZLMZGatl2ZhVnLysfsknVDi9U9IWuacRimJL9en+zhaSXw6XNL7Jf08/Qx/L+nHko6X9Jl02wskPeOclpXpTydJH1ESY/4p/a7OkzTP+67ureAztJg0vv5E0hda87honxo82c3UXdK7JH1MyX/w97x+qpn+aqZJZpprplvNZNF7u5rpATNdUWK//2qmZ830gpm+nnP8/zbTHDM9YqYB6WvjzDQ1fe+9ZupT7nUzXSRpgqRbzTTTTF39/Tunl53TvBKHPl/SHc7pbee0UNJ8SRPTv/nOaUEaOO9It90hqWsafDpJ2iXp3yV9Neez9ZB0tHOa5b18oaTfp/v1v+9fm+lHZnraTAvSzxXv7x1mmmGmkdHrA8x0d/p9P5tzgXJQ+v/pq2Z7+22mz5ppdvp3TQOvf1vSyPS7/m762u+UBGSgNTwpaZSZhptp9p4XzfQ5M30t741mutRML6b/pv8zfe2fvH/LMtPlZvpJWv6wmaal/95/secGykybzPQ9M82SdKJ/jBqMOXv6tULSa5KGmelrZvqc977ZZhqes98uZvpV+t3NMNNp6etTzXSEt91fzTTBTN3MdHP63c0w0/ned3u/mR6V9Ej6NuIH2gwzjTLTS2a6VdIcSUPSOLEnrnwr3a6jmdZ577vETDd65dlmmmWmx7ztv5+eMy+Y6ePp62ek59UfJL0Y98c5TZFK3vScL+mWtHynpPd5r9/unLY7p9ckva7kh94dkuokdZG0y0ydJf2zpO/lfB3vlTTNOe1q4Dubmn626ZI+aaaRZno8/ZwPmSU/ipvpDjOd671vU/q/B5npqTQOv2im49PXz033PcNMt++5/jPTMjNdb6YZks5L4/FwM/XN6yfQkEp+QTlf0p+d0yuSVpvpOK9tvKRrlPyiMUIKLta7K7k5uN05/Y+/QzOdKWm0kouFcZKOM9PJJY7dTdJ053SEpMe192LhfyVdm/5C+2Le685pkqTpkv4+/VVkawWfWZKGSnrDqy9JXyv5unN6WdJKSc+nn3uUpP2c0/M5x5gg7b3oS10q6fb0L34yOETJDe65Sm5eMmZ6p5Jfy85PA6Hvh0p+RXuHpL+RksBdwsS0/WhJf5te/Bwn6aNKfp06QdIVZhpf7nVJn5f0Wvpd/2u63+mS3p3zPTS3Xzbjdq29r7bc96qz5FfOs1Xi4qKC9x4g6T8lvUdJXHqHmT4o6W5JF3ibXizpDjMdlpZPSn+B3aW9F//dlPyCe4xzmlxhF6oVcyRJljwZH6HkZq6xPqVkNPlRSuLWLWbqIum3kj6U7n+IpCHOabqkL0l61DlNlHSapO+aqVu6r2MlXeScTknrrR0/mqIWz8e2HEvaRLzJMVbJf3MPl2SSvqHk3/l4SSf5NwZlfFXS6c7pGO2NPZ+QtCI9Z94h6VNmOjhtmyDpKud0WCP6mMWV9MeazZaMlCkXh/5P0kWSHpT0TUlXS7q5gWuqkyQ9V2F/zDlNcE4/VnIt87P0Wu53kr7fwHs/IumeNA6PkzTHTIMlfU7Sac5pvKS5Sm4C91jqnMZ7T8pmKvoBrIbV6vlYi/tqzuM1qJLHrJcquSiXkl9IL9Xek2Sac1oiSWaaKWm4lF1A3CfpO86p1EpvZ6Z/M9J6dyU3WU9E2+1W8h9lKTmh7zFTL0m9ndPj6eu3SLqr3OsVfL5m41zwFOf3kq4005ckHSPp4fjmUslN0krvPYOUfA+TnZMz0w4zHelcdhH0O+e0W9JLFg7ROUzJP4gzndNbJbp2hqTDbe9zxJ5m6u5c8iuP52HntDrtyz1KbuCcpHud02bv9Xcr+Q9FqdfvL3H8FSo//LLZOecqOjkq2a6199WW+15lXdMYJCVPqm5S4//NvUPSX9MhZ0p/aT7ZOf3OkqfDJ0h6VckF01NKbiSOk/Rsem51VfJvXUpusO5u+sepTNGYk7rYTO+S9LakK53TGjM11ruUDA2Sc5prpsWSxij5BfwhJReJH5I0Kd3+TEnneU/DukjZBeLDzmmNt+9WjR9NUYvnY1uOJW0g3jTktfTHAyn54fFR55IkeDPdJulkSX/Oef9Tkv7XTHdJuid97UxJh5llI1h6KblekKQpzun15vwAMee0VtI5kmSmfkqGy12UPl3rreR6b1r0tiHae53XkDu88gQln1dKruW+3MB7pykZLt1NyXXSC2Y6S8kP/lPSeNZZ0l+99/w22kfNx5k9avV8rMV9NefxKpF7U5U+Cn2PpKPM5CR1kOTMsicQb3ub74r295Sks8x0m3OKpxg0Sdc7p180sr+tOVXhm5IO8uoHpq8p53VJUjqU5TklN4sjndOHzPSgmW51Tlu8TbcquZjY40OS+khamAaBnkpuYr+Utvvft3/ZszTdz3ip5E3VfpJOcE7bSn7SveLvt7m+7y5SxU8IgabYmv5KmTHTToVP47uo6e5Qcn7OVfJjgrNkuPMtzpUci7+toSEvJVQj5khJvsPV0WvN8t05pzfNtNqSPI2LtTfXzST9TTwMMh22sznaDfEDbU38b7iU3Qr/O+6fY1couRk7V9Lz6SgQU/I06hFvO5npjAqPF9sTb5alQ/m6Oad1ZrlxaI+vSvoPSR+W9JiSH1PvVDJKwFcq3pRTyWfI4pIl+Vr7SZJzetiSIcfvV5Lq8Q0l3+8fndNHKzwecQaFNTT87yJJv3FOw5zTcOd0kKSFqmwoxnWS1ipJgIw9KOkfLcnXkpmGmmlgmf7tyR36OyVPcNZLWmuW9eEySY+Xez0tb5TUo4I+++6XdImZ9jfTIUp+EZom6VlJo810SBqILpH3dCY90a+R9B0lv1zvuTHpoOSXEt/LSobs7HGppLPS73q4kl/BL1HD1ikJJteb6dQS7Q/Je+xtFl58et5rpr7puOMPKrkxflLSB81Ul/4KdEH6WrnXS33XY1RmyBHQgpZLGmimfmbaX2pwyM00SaeYqb8luVGXam8MuVfJUOhLtfcX1UeU/FI7UEp+hDLTsAL9rUbMKWeRkmF4MtOxkg5pYPsnlQ59NNMYJU+d9tww/VbSv0nq5ZxeSF97UNI/pzemSi8ayyF+oC17RtJpaRzqqOT8fTwddbLWTKMtyYv0hxiPcE5TJX1FyXXUUCXnzFXpPmSmQy3KEW+k+yX9Q1r+kJLrhD2vX2qmzpbkZw+TN4TPTGMlDUiHNdcpuXlxUsm+VBpvYs9q77Wffy23SMpSUP5GyuLHcCXD+X6h5MnWeCWjpk5P22Sm7ma5fSHOoLCGbqoulcKZWZQMaal0FsAJBqSJAAAgAElEQVRPKxmW8x3/Ref0kKTblDyWfVHJkJBSNz2bJU20JNn8PUqSsKUkEHzXTC8oGT/b0Ou/lnSDlZiowkwXmGmJkrG0fzTTg2kf5yj55eUlJY/pP+WcdjmnnUrGEj+oJGDcmW67x6eU/Hq9RdILkurSz/icc3uTUtNjzFUyy1+P9MQfJmmq175Q0vr019tczmm5kovGn5bY/l8kTbAk6fMllZ8ZbZqS/39fkHS3c5qe5mf8Om17RtKNzmlGzuurJT1lSZLtnuT+0yT9saHP0BzM7Cwzm2dm883s82W2udnMVphZ2QBqZgeZ2WNm9pKZzTGzT5fYpouZTTOzWek2OROuWAczm2Fmf8jZZpGZvWhmM81sepltepvZJDOba2Yvm1m9MeBmdmi6jz1/G8zsmhLbfSbt92wzu93MSv6iaGafTreZU2o/tco57VASA6ZJeljJU6a87ZcqyQl8TNIsJefsfWnbWiXn+7A9Q1yc00tKhqU8lMach5UMd8lVKzGngW7eLamvmeakx36lge1/Jmm/9Li/lXS5c9mT9UlKLiTv9Lb/DyWTa7yQHuM/cvbdavGjsVoz3qTb1VzMqTTepNs2GHPaarwpJ02R+IqSoWczJU11Lvv3fK2S8/ppJblLe/x3ei69KOmxNAXgF0qGH89Mr4l+rgpSOMz0fSU3Iz3NtMQsG0r3SyWTaMxXco5/Me3vLCV5TC9L+pOSp2O7vV1+U3tHz9ym5PriGUk/KHH4P0lZbmRjfFLS1WlcvVDS/0tf/7mkcyyZAOhwKRsN8F5JsyyZeOIDkn6axvMrJE1K9/OUytzgWZL/OVTKfvSpWVzjNO81TrPHm+aYl52/pv9J7jOS+3i1+9HCn/EJyfVp+eOog5LZy0Yo+YV+lqTDS2x3spJf4cuusaPk4vjYtNxDyUXl4dE2Jql7Wu6k5D8sJ5TZ32eV/Aeo3ppi3jaLJNVb3y3a5hZJH0/LnSX1ruA7WaZk4Tr/9aFKnjp3Tet3Srq8xPuPVPLrXZ2S/4D/RdKoav+b4q/pf20t5rRW/Gh8v1o33qRtNR1zysWbtK3BmEO8aX9/krtfciOq3Y8G+nip5L5U7X403E+uccp8J026xmmJeNNq6yegrJ8rzJVqVyyZBv/7Lvmlv6WlU0+7Bc45f+rpgHPuCSlIhK/HObfUOfd8Wt6o5Fe7odE2zjm3Z7KPTulfvTw0MztQyfDMcrMuVsTMeikJljelx9/unFuX/y6dLuk151yp1dk7SupqZh2VBJVS+XiHSXrGObfFObdTyTCMC5v6GVAT2kzMaeX40VitGm/StlqPOXnxRmo45hBv2p9rVfsTQDjtnZCtlnGNU1+Ra5xmjzfcVFWZc9rmnH5T7X60FOe00uUvRNqcyk0FW4iZDVcyRvuZEm0dzGymkpmDHnbO1dtGybCIf5OCIRSlOEkPmdlzZvaJEu2HKJm57VfpY/Ybzaxbie18lyiZnj88kHNvSvovJWuQLJW03jn3ULydkl9x3m1m/cysTsnsTweV2A5tRFuKOa0cPxqr1eNN2l7LMadkvJEqjjnEm3bGJevyVbqsRFU4pztc/dmQaxHXOPUVucZp9njDTRWQw8y6K8kvucY5tyFud87tcs6NUzJD0kQzOzJ6/7mSVjjnKlmr413OuWOVzKD0KTOL127rqOSR/s+dc+OV5ByWHFOdHruzktXr6y0tYGZ9lPzCdYiSXxG7mdmHS3y+l5Ws3fSQkjyfmVKjZ7UDUIGG4o1UuzEnL96k7Q3GHOIN0Lr25Wuclog33FShPalkKtiKmVknJcHmVufcPXnbpo+oH5N0VtR0kqTzzGyRkkf17zGz/yuzjzfT/12hZIKYidEmSyQt8X4pmqR0hrYyzpb0vHNueYm2MyQtdM6tdM7tULIWyjvL9Osm59xxzrmTlcxE1dCkBcC+oGrxRqrJmJMXb6QKYw7xBiiLa5xQ4Wuc5o433FShPUmnnrZD0l8wgqmnG8PMTMm43pedcyVXczezAWbWOy13VTIDUTDDnHPuC865A51zw9P+POqcq/dEyMy6mVmPPWUlCx/Ojva1TNIbZnZo+tLpSmaKK+dSlRmKo+SR+AlmVpd+1tOVjKku9TnTKcPtYCXjjW/LOSawr2jVeJNuV8sxJy/eSBXGHOINUBbXOKHC1zjNHW8anI4TaCucczvNbM/U0x0k3eycmxNvZ2a3SzpVUn8zWyLpq865m6LNTlKyPsaL6XhiSfqic+5P3jZDJN1iZh2U/EBxp3Ou7HSiDRgk6d7k3FdHSbc55/5cYrt/lnRrGlAXSKUXNkyD1nslXVmq3Tn3jJlNkvS8kgUVZyiZYreUu82sn6Qdkj5VQeIo0O5VId5INRpzGoo3UqNiDvEGKIFrnL2a8RqnWeONOVdvIg8AAAAAQIUY/gcAAAAABXBTBQAAAAAFcFMFAAAAAAVwUwUAAAAABXBTBQAAAAAFcFMFAAAAAAVwUwUAAAAABXBTBQAAAAAFcFMFAAAAAAVwUwUAAAAABXBTBQAAAAAFcFMFAAAAAAVwUwUAAAAABXBTBQAAAAAFcFMFAAAAAAV0rHYHgMbq37+/Gz58eKPfN2uWtHNn+faOHaVjjml6v/Zlzz333Crn3IBq9wNobk2NN2hZxBy0Vy1xjcP1TTGVxhtuqtDmDB8+XNOnT2/0+8zy23fulJqwW0gys8XV7gPQEpoab9CyiDlor1riGofrm2IqjTcM/wMAAACAAripAgAAAIACqjr8rxbGqu/0BqCuXLkyaOvQoUNQ32+/8veg8bZ5nHNBvWPHvf839OjRI2izhsasNbNFixZp1apVrXtQoBXUQrzJs3nz5qC+e/fukuWGxNt26tQpqHfv3r0JvWs55Magvaq1mDNv3rygHl9f+PX4OqVz585B3W/fsWNH0JZ3rRTvd/To0Tk9bn5c46AlVfWmqhbGqvs3Ur/4xS+Ctt69ewf1rl27lt1Pr169grofnHbt2hW0bd++PagPHDgwK5966qlBWxzIWtqECRNa9XhAa2lMvIlvTPyLhPiiwFfkR5ApU6YE9S1btmTlOGbEMcX39ttvB/UBA8L7lZNPPrmpXWwR5MagvaqFaxxffH0R/xi8//77Z+Vt27YFbfHNod++fPnyoC3+cdiPV3Hs+tOf/pTf6WbGNQ5aEsP/AAAAAKAAbqoAAAAAoIB9fkr1u+66Kyt/4xvfCNr69OkT1IcMGZKVFy5cGLQNHTo0qI8ZMyYrv/zyy0Fbly5dgvoZZ5yRlePH6JdddlnZvgNoGfEwvqYO+du4cWNQf/TRR7Py888/H7Q98MADQf3QQw8te4xNmzYF9dWrV2flfv36BW3xMJ5vfvObWfkDH/hA0Hbeeedl5YMPPlgA2rYNGzZk5Tlz5gRt8dBg39atW4P6a6+9FtT965g4h6quri6o+8OX844JtHU8qQIAAACAAripAgAAAIAC9vnhf/7sf/HsNnnTgg4ePDioxzPa+MNx1q9fH7T17NkzqL/55ptZeezYsfkdBtDi8ob/5Q33++UvfxnU4ymM/VkF43P94osvDuozZ87Myv6sXFK4FIQUDhWMZ97q1q1bUPdj3uLF4cR7n/nMZ8q+79vf/nZQP+CAAwSgtvnDf+PYFV+3+LMNxzMPx+kQ/nv9IYZS/Wsn/9oqbxZloK3jSRUAAAAAFMBNFQAAAAAUwE0VAAAAABSwz+dU+blP8VSf8RSiffv2zcrxVMlxHsO6deuycjwdc97446OOOqqSbgNoQfE5m5dH9bOf/Swrr1mzJmg75JBDgnqnTp2ycpzPMHDgwKB+yimnZOV77rknaItzOv38hzifIY4p/tTto0ePDtp69eqVleN8qy9/+ctB/eabbxaA2nb33XdnZf96R5IOPPDAoO7HJD//U6qf1+m3x9Ovxzmffl75W2+9FbQ999xzWfm4446r/wGANoQnVQAAAABQADdVAAAAAFAAN1UAAAAAUMA+n1M1bNiwrDxr1qygrUOHDmXr8Rou8ZoO/tjkOP9h7dq1QT1v7RoArS8vp+qNN94I2vz6iBEjgrZNmzaVPUYcQ5YvXx7UR44cWbIsSa+++mpQ9/M9jz/++KDtiSeeCOr++lL+GjaStGXLlqwcryezbNmyoP6b3/wmK1922WVBW2Ny0gC0nBtvvDErDxkyJGiL8zj9GNSxY3h5GMe9urq6rBxfK3Xp0iWo+/tasWJF0DZt2rSsTE4V2jqeVAEAAABAAdxUAQAAAEAB+/zwP39YSjz1cDw8xx/SEk+3njekb8yYMbl98If2xI/cAbS+eFpy3/z584O6P/Qlnkq4e/fuQf3tt9/OyvGU6vG2/rIMZ599dtA2efLkoO4P1Yv7ENf94cibN28O2vylIrZv3x60xVMqz5gxIyvHw/8Y7gfUhnnz5mXlCRMmBG3xVOg7duzIyvGU6nF88uNDHGP8pRniehxb4ynWgbaMJ1UAAAAAUAA3VQAAAABQADdVAAAAAFDAPp/A44/vPeigg4K2ww8/PKj7eQJ33XVX0LZmzZqgPmfOnKx88sknB23xtKFDhw7NynEegz9tKYDq889tKZw+2M+ZkupPLe6fz3HOQjwt8YYNG7JyPBXymWeeWfa98X5GjRpVtk/xNOl+bkQ83XrMnwoZQG1YunRpUPdzN+Mp1OPpzf3roXiZmHhKdT/uxflWfm6WFMaVeLr1OFcTaMt4UgUAAAAABXBTBQAAAAAF7PPD/w477LCs/Mgjj5Rtk8LH1EcccUTQNnHixKD+iU98IisffPDBQduBBx4Y1Pv06ZOV/amRAdSeJUuWBPWePXtm5Xj4X2zQoEFZecuWLUFbPC1xp06dsnI85DBe/sFf0uGAAw4I2uIpi/2p2pcvXx60+dOtx8c85JBDgnq/fv2ycjxsOR46BKB1xEN681II4uHJ/vXHqlWrgrZ4OvbZs2dn5U2bNgVt8XBAfwhiPDw5Hg4ItGU8qQIAAACAAripAgAAAIACuKkCAAAAgAL2+ZwqP6+hW7duQVs8NtnPfYrF+RB+bkU8dXI8hrhjx73/N8TTGDPdKFB9ce6Rz88n8POVJOnoo48O6n6elJ9nUIqfexDHgfg4fk5TnCcRT2/sT7kc79ffT3yMmB/XXnjhhaAtzr8A0DpeeeWVoO7HnPgaJ+YvGxNPzf7aa68F9fHjx2flefPmBW3Dhg0L6n6OpX+9I3GNg/aFJ1UAAAAAUAA3VQAAAABQADdVAAAAAFDAPp9T5Y8xjteN2W+/8J7TX+8lzqEaN25cUPfHJm/dujVoi9d08XMr/PHPAGrDggULsnK8BoufB7l58+agzY8DkrRmzZqsHOc6xfmUvjgvM17rxT/OihUrcvvgHyfugx8D488Zr8Hl50YsXLgwaCOnCqiOuXPnBnV/nao4PsVxxM+jHDBgQO5xTjjhhKw8c+bMoC2OOX7siNtY0w7tCU+qAAAAAKAAbqoAAAAAoIB9fvhf165ds3I83C8e/pLX5k8vGouHFfrHlMIpRRn+B9SeN954IyvHSyLEQ/N8ixcvDurDhw/PyvGwl3iKdX+IcY8ePYK2OE74x4n7E09h7Pc/Pqa/jEQ8/XJ8TL8eT6kMoDrmz58f1Hv16pWV49SD+Jz2Uxwuv/zy3OP84z/+Y1a+4YYbgra8mBgPOYzrQFvGkyoAAAAAKICbKgAAAAAogJsqAAAAAChgn8+p8sfzxuOL46k//XpevpUU5k3F0xbHuQp+HxhfDNQeP9cgzr3s2bNnVo6nHd+4cWNQ998bL8sQn/t+e3zM+Dh+DkOcf7V27dqg7udUxcs9+J9l5cqVQZufmxEfc9asWQJQfRs2bAjq/rVIfE0TX5v49WuuuSb3OO94xzvK7jdvCQg/hzxuA9o6nlQBAAAAQAHcVAEAAABAAdxUAQAAAEAB+3xOVf/+/bNyQ+OC/TUe4rVqYn5eg3MuaIvfO3To0Kwc504AqL5NmzZl5Xh9qT59+mTleF2q888/v+x+4ngT53T6eVNxDlWcC+GvRRW3bdu2Laj7x41j0dixY7PyfffdF7TFscnvb5ybBaA64nXp/PzvOObE5+3gwYOz8ogRIyo+pn8dJdW/durbt29WXr16dW4fgLaMK3gAAAAAKICbKgAAAAAoYJ8f/jdkyJCs7A/vk+oP29uyZUtWjofYxPzpkOMp1P1pi6X6j8oB1BZ/CJ0/RbFUf7iN7/DDDw/qTz75ZFZuaFkGf7jdunXrgjZ/yGG8bTyMMO5fHNd8Y8aMycrxsJz4ff7UyOvXry+7TwCtp1+/fkE9XrrB5w9HlqSzzjqrScf0hw1K9adJ94cHrlmzJmjj+gftCU+qAAAAAKAAbqoAAAAAoABuqgAAAACggH0+p6qurq5kWaqf8+CP/Y3HBcf8PKp4OmQ/F0GqPwYaQHXFeQh+DuWuXbuCNj/XKM5nOuCAA8puG/NzNqUwF2rz5s1BWxwz/KmS42mT4yngffFnGTVqVNn+xNv630mcmxHXG8ofA9A84nNt7dq1WTmOa/Pnzw/q3/ve98ruN8598vM4DznkkKBtyZIlQX3AgAFZOY4j8bZAW8aTKgAAGmPwYMms/F+UuA8AaP+4qQIAoDGWLy/WDgBod7ipAgAAAIAC9vmcKn89hXg9qXgMsT8e2R8jXMro0aOzcrzeS7zGlb8GDoDqW7VqVVD3c6HinCU/RyDOqYpzGPx6vH5UvE6enxsR53vGMcSPMQMHDgza4jVj/M8St/k5YH7ORCn+el3xd7Js2bKg7udqAWg5cc62f30R5zrGOZ7xunq+OBfKjw9HHHFE0LZw4cKg3qNHj6y8cuXKoC1ecw9oy3hSBQAAAAAFcFMFAAACeXNxMA8HANS3zw//88VDdeJp0/32hh5Z+4/R33jjjaBtw4YNQd0fRgOg+tatWxfU/XO/S5cuZbc9+OCDgzZ/2IsUTo0+aNCgsseQwuHH8TC9eMiwP/wv3jYeZugPHdy4cWPQ5g8PivsX79cfDhQPI1qxYkVQZ/hf25M31wbzcNSuo446Kqg/88wzWTmOG36agiQNzrlbzhsOfM455wT1H/3oR0HdX54hHhrct2/fsvsF2hqeVAEAAABAAdxUAQAAAEAB3FQBAAAAQAHkVHlWr14d1OPxxg888EBWvvLKK3P3deyxx2bladOmBW1Dhw4N6vHU7QCqK54i3J/ePJ6yeN68eVl57NixZd8n1c9v8sVTFvu5T3F/4j74eZlx3kS8X39fcd6ov6xEnJsR51/5eaVx/+KpmwG0josvvjio/+pXv8rKcfyJ87sfffTRrHzmmWcGbXHepC+OewcddFBQ9/Ox4v3EcQVoy3hSBQAAAAAFcFMFAAAAAAVwUwUAAAAABZBT5Xn88ceD+vz584O6n1P1m9/8JndfRx55ZFaO8xZ+8pOfBPVjjjkmKx933HGVdRZAi4nzK/1cBH9NKElav359VvbPZUlauXJlUPdzGOI8JD+HSpLefvvtrFxXV1e2P1KYsxD3L87Z7NSpU1aO1556/fXXs/LIkSODtqeffjqo+8eJcyriXA0ArSM+p/3zPc51jLf1r2vinKq8fND+/fsH9XgtqsWLF5ftQ7zuH9CW8aQKAAAAAArgpgoAAAAACtjnh//503vGUw/Hw//8KdYbemTtPyr3hwdJ9adY37lzZ2WdBdAqnn/++aDuD3WLh9ctX748K/vTjEvS9OnTg7o/jM8fsleq7sejzp07B21xzPC3jeNYPP26X4+H9MyaNSsr9+zZM2jzp22Xwu9hy5YtQVv8uS+66CIBaH3+cLs4dsXXMfG1SVPFyzo899xzWTke5hz3CWjLeFIFAAAAAAVwUwUAAAAABXBTBQAAAAAF7PM5Vf60xtu3bw/a4rG+cW5CHn9f8RjiOMcqbgdQXd26dQvqfo7Am2++GbRt3LgxK8dTqvs5SpLUu3fvrBznIcX8fE9/enWpft6UPzVy9+7dg7Y4H8vfNp7WfdGiRVn5vPPOC9o+9rGPBfUPfehDWTme8n3IkCECUH0nnXRSVr7tttuCtr59+wb1OFY01fDhw4P62rVrs3JDsQxoy3hSBQAAAAAFcFMFAAAAAAXs88P/fPGj7w0bNgT1eEhQHn8V83ja4ni43+DBgyveL4CW99GPfrRsmz9FsSQtWLAgK48cOTJou+eee4K6P+V6vJ/du3cHdX+o4KpVq4K2eKiyPzwxnm49rvtTtw8cODBomzp1ala+8sorg7aVK1cGdX+YYUNLTACojquvvjorT5o0KWiLl3FYt25dVvbjmiSNGDGi4mP26NEjqPtDpOM4Fy9DAbRlPKkCAAAAgAK4qQIAAACAAripAgAAAIACyKnydO3aNaj7eQpS4/IG/Pwsf2pkqf6Y4uaaxhRAy4unLD/66KOzsp87IEmrV68O6v4Uxg3lVvpTrsf7iWOKH0PiPIl4CuO8eOMfc+bMmUHbOeecU/Z9AGrT0KFDs7KfpynVz+v0czWnTZsWtDUmpyqOMX5eZxyP4vxQoC3jSRUAAAAAFMBNFQAAAAAUwE0VAAAAABRATpVn2bJlQX3Xrl1BPc6FyuPnXcQ5DvF+41wuALXFz2GK40CHDh2y8uTJk4O2eI06X11dXVCP9zt//vys3FA+gx+74v3EuaH+entx7PHzL5544omgLc6p8r8TM8vtH4DWEedb+ufme9/73qDt7rvvDup+LtR9990XtF1yySUV9yHOO33rrbfK9q8x11VAreNJFQAAAAAUwE0VAAAAABTA8D/PoEGDgvqKFSuCuj/MpyF9+vTJyg1NcTxw4MCK9wug9flDaPLiwLx584J6PIWxf+7HQwPj9x5yyCFZ2R+yJ0lvvvlm2f3Gw2m2bt0a1P3hN/HUx349Hg4d87+TvCFHAFpP3vDkeAjvpEmTgro/HHjJkiVN7kOvXr2Cuj9tun9tJElr1qxp8nGAWsOTKgAAAAAogJsqAAAAACiAmyoAAAAAKICcKs/ZZ58d1KdPnx7UG5NT1aNHj6wcjy+OpzgeNmxYxfsFUF3xkgh+XFi8eHHQFudPjhkzpuT7JGns2LFBvW/fvln5pZdeCtrinKUdO3Zk5ThXy49FUhiP/FyHuL9btmwp2yZJ+++/f1YmpwqoDXEOt+9d73pXUPeXUJCkdevWZeU4p3LWrFlB/Zhjjil7nJ49ewZ1P5Z06tQpaIvzToG2jCdVAAAAAFAAN1UAAAAAUAA3VQAAAABQADlVni5dugT1OPepMTlVvnidmDhX4cADD2zSfgG0vrx8oW9961tB/bvf/W5Qf+CBB7Kyn78ghetSSWFuVBxD4rXt1q5dm5U3bNhQtk0KcyXifIb+/ftn5auvvjpo83OoYnl5HABaT2PyGQ8++OCgPnPmzKwc5z49/PDDQT0vp2rjxo1BPY5fvuXLlzfYT6Ct4L+EAAAAAFAAN1UAAAAAUADD/zwf+chHgvrkyZODejzleqXOO++83PajjjqqSfsF0Pryhrp17do1qF933XVlt3399deDejxtuj8sJh7St3v37rL7jYftxHV/yM9JJ50UtHXv3r3sfgG0L1/60peC+uDBg7NyHDdOOeWUivd78cUXB/VBgwZl5XjI8emnn17xfoFax5MqAAAAACiAJ1UAAFTJ5z//+bJt3/72t1uxJwCAInhSBQAAAAAFmHOuegc3WylpcdU6gFKGOecGVLsTeSZMmOCmT5/e6PdVMtNsFU+HNs3MnnPOTah2P/IQb2pWTceckvGmGYNJrT6paugjVjtWEnPQRDUdb6SWu8ap9jnbllUab6o6/K/W/2EDaD+INwBaEzEH2Lcw/A8AAAAACuCmCgAAAAAKqGpOFdAUFYxT7y9pVQW7qmS71t5XW+17bY1TN+sn6ZG0NljSLkkr0/pEObc9572nSvqcnDu3RNuNkr4v514q0XaNpF/KuS3ea5+X9IakzZJeKfm+pjI7WNJLkr4m5/4rfe3Tkq6QZJL+R879IH39PyWdLWmmnPtI+tqHJfXPtqm//yHpPs6VWZ2k/5F0dLrvdZLOUvLv4g9y7sgS7/93SU/Iub+UaLtc0kNy7q20foekr8i5Vxv9PbSwCvNiau18rMbxmnNflWxTWzEHaCY1eI3T3mNJJdtVFm+ccy3yJ7l+kpuZ/i2T3JtevXNLHbcJ/fy25JZIbl30ehfJTZLcfMlNkdzBXtuX09fnSu6M9LVBkntKcrMl9wFv299LbnDO8T8nub+T3A3pd/OS5LZ639UFNfAd/Vhy76x2Pyrvr6Y313atva+23Pea/ZO+5qTPNWL7U530h0Yeo4OTFjmpf/T6Y04a4KRfO+miZv5ck5x0V/bZpCOdNNtJdU7q6KS/OGmUk3o56eF0mxuddJSTujrpESd1ytn/d510flr+gpO+77Ud6qT9nTTcSbOb8F391UkTvNdOcdL/VP3fShP/avF8bMuxpC3EG65xsm25xmn1/tbm+ViL+2rO41Xy12LD/5zTauc0zjmNk3SDpP/eU3dO2yXJTGbWekMQzUpOzHGfpBNKvP4JScuc0yhJP5V0fbqPoyVdKOlwSe+X9PP0M/y9pB9LOl7SZ9JtL5D0jHNaVqY/nSR9RNJvndM/pd/VeZLmed/VvRV8hhZjpg6SfiLpC615XKBVmJ0is5np3wyZ9UhbustskszmyuxWWTqvktlfZTYhLW+S2fdkNkvSlyQdIOkxmT2WtveU1FnSaCXn9XfT44yU2TiZTZXZCzK7V2Z9vP3/MN1utswmlun3ByUtlDTHe/UwSc/IuS1ybqekx5XEqt2SOqWfoU7SDkmfk/RjObcj59v5G0l/TstDJL2ZtTg3T869ndY6yOx/ZDZHZg/JrGvax1/L7KK0vEhm/ymz5yVdKmmCpFvTz9lV0pOSzpAZayeiTeAah2scINbqOVVmGmWml8x0q5ILgiFm+rCZXjTTbDN9K92uo5nWee+7xEw3euXZZjoOXf4AACAASURBVJplpse87b9vpmlmesFMH09fP8NMfzXTHyS9GPfHOU2RSgaE8yXdkpbvlPQ+7/XbndN25/SapNclHafkQqVOUhdJu8zUWdI/S/peztfxXknTnNOuBr6zqelnmy7pk2YaaabH08/5kJkOSLe7w0zneu/blP7vQWZ6ykwz0+/5+PT1c9N9zzDT7Wbqmr6+zEzXm2mGpPOc0zxJw83UN6+fQBv0OUmfknPjJL1b0tb09fGSrlFyYTFC0kkl3ttNyU3MMXLu3yW9Jek0OXda2n6GpEfk3NOS7pf0r3JunJx7TdL/SrpWzh2tJC591dtvXdqfqyTdXO+oZt0lXSvp61HLbEnvllm/dLjeOZIOknMbJf1J0gxJSyWtl3S8nPtd2W/F7BBJa70bp5slXSuzKTL7hsxGe1uPlvRTOXeEkmGBf1Nmr6vl3LFy7v8kTZf09+n3sVXO7ZY0X9IxZfsEtAFc4wS4xsE+pVoTVYxV8qvO4UrG539D0mlKLmRO8k+aMr4q6XTndIykC9LXPiFphXOaKOkdkj5lpoPTtgmSrnJOhzWij0OV5EIo/dVps5l6+6+nlqSv/Z+kiyQ9KOmbkq6WdLNz2UVaKSdJeq7C/phzmuCcfqzkV7GfOaejJf1O0vcbeO9HJN2T/ko0TtIcMw1WckF5mnMaL2mukgC5x1LnNN77FWmmpBMr7Gu1/bIZt2vtfbXlvrdFT0n6vsz+RVLv9AmPJE2Tc0vSi/2ZkoaXeO8uSXfn7PssSQ/Ue9WsV3qsx9NXbpF0srfF7ZIk556Q1FNmvaM9fE3Sf8u5TcGrzr0s6T8lPaTkCdPMtI+Sc99Jb2D+n6T/kHSdzD4usztl9uUSfR+ivTloknMzldxcfldSX0nPymxPPF2YtktJPBteYn+S9Nsyr++xQsnTvraoFs/HthxL2nq84RonwTVOy6jV87EW99Wcx2tQtW6qXnNOe1Y2O17So85plXPaIek2hRcYpTwl6X/TX2r2fIYzJX3UTDMlPSOpt5JfUCVpinN6vVk/QcQ5rXVO5zinCUp+LXqfpPvMdKOZJpmp1DCe8MIl3x1eeYKSX5ak+hdkpUyT9E9muk7SEc5pk6R3KfkVfkr6nV2s8GIovgBqMxc8zrmKTo5KtmvtfbXlvrcJZp/yhvsdIOe+LenjkrpKekpmY9Mt3/betUul1/TbJufyfoGdqOTca6x49qC4fryk78hskZKnaV+U2dXJlu4mOXecnDtZ0lpJrwTvNBuv5CJvnqS/lXMfkjQyevIkJU/suoS9cJvk3D1y7iolF1jnpC2VfFdSMllHni5S7gVazarF87Etx5J2EG+4xklwjdMCavV8rMV9NefxKlGt8esN/cdVSvIA/PWh/f/AX6EkUJ0r6Xkz7blQuMq5bMYvScmj8QqPF3tT0kGSlqWPubs5p3Vm2et7HCg/1yDxVSW/Bn9Y0mNKhv7cqWT2LV/9C5fyKvkMO5UG4HQs836S5JweNtNpSsZH32qmbyj5fv/onD5a4fHa7AUPkHHup0ryBxJmI+Xci5JelNk7lPzCvK7MuxuyUVIPSatkdoSkud5N1542ybn1Mlsrs3fLuSclXaYk/2mPi5XkZr1L0no5tz76DO/2+v81SZvk3E/S+kA5t0LJzIAXqn4uxX8o+cW7k6QO6Wu7lQzr8b0i/wLE7CRJL8m5tTLrrORi5a8NfSE59n4fe41RMoQRaOu4xklwjYN9Si2sU/WMpNPM1M+SBMVLJD3unHZLWmum0ZYkSV7gvWeEc5oq6StKfo0dquSR9FXpPmSmQ/eMn22i+yX9Q1r+kJIhNXtev9RMnc00UtIweY+3zTRW0gDnNFnJhcpuJb80l+rLy5JGNaFvzyp5DC+FF2SLlIx9lpK8Bkv7NFzJo+5fKPnVZ7ykyZJOT9tkpu5muX3hggft0TXphBAvKMkZqD9cr3K/lPTndKKKs7V3kgcp+RX2X9PJMEYqiS3fTY87TtK/e9tuk9kMJUNgPtbIPtwts5ck/V5JrtjeG8Rkcovpcu6t9PWZMntRUhc5NyvYi3ObJb0msz0xYaSkx9PtZyjJicob+tiQX0u6IZuowmyQpK1yrmTCO9CGcY3TeFzjoG1qjikEG56q0H1Ncp9Ly6MkNzNq/7DkXkyn6rzee/1iyS2Q3FTJ/VRyN6av3+9t//30tQ7p1KGz079HJddDcmdI7nc5fft+Ot3o7vR/v5y+3lVyd6fTik6V3HDvPddJ7jXJzZPcmdH+7pbciLQ8JJ2qdI7kPlji2CMk91j0WqnvZ6rkjvTqIyX3uORekNxDkjsgfX2o5J6V3CzJ/bvkNqWvX5F+JzPS9x2Uvv4+yU1P9zNLcmelry+TXHfveF2UTIO6X2v8eyn2b01nKRnaNF/S58tsc7OSR/1lp4JW8kvdY0rWAZoj6dMltumiZNjBrHSbr+fsr4OSi9GyU3Ur+Q/Gi0rGdpec3lPJkI9JSsaHvyzpxBLbHJruY8/fBknXlNjuM2m/ZyvJ4+lS5pifTreZU2o//JX4kx520pAmvC+cary6n+ECJ32jlY71GSd9rOqfuZF/rRlv0u1qLuZUGm/SbRuMOW0x3nCNwzVO6/1b4xqn0phTjXhT9X8g+/pfGjxHVLsfDfTxUsl9qdr9aLif6iDpNSUJ9Z3TQHB4ie1OlnRsAwFniKRj03IPJcOhDo+2MUnd03InJb9InlBmf59VMpa+oYDTv1x7us0tkj6eljtL6l3Bd7JMycJ1/utDlUzJ3TWt3ynp8hLvPzINOHVKhgv/RdKoav9/3W7/aummKunPx1vpOB91Useqf95G/LV2vEnbajrmlIs3aVuDMYd40/7+uMZpzn5yjVPmO2nSNU5LxJtaGP63r7tWtZ8c6ST9sNqdqMBESfOdcwucc9uVDLk6P97IJbOqrcnbkXNuqXPu+bS8UckvJkOjbZzbOwNbp/TPxfsyswOVjPW+sdGfKNxPLyXB8qb0+NudP7yrtNMlveacK7U6e0dJXS1ZG6hOyZTgscMkPeOc2+LCtY/QEpw7Vc5Nb3jDVuJcoX+zjTjOr7R35sW2olXjTdpW6zEnL95IDccc4k37wzVO8+Eap74i1zjNHm+4qaoy5/SyS8Ym1yzndIdLZtOpdeWmgi3EzIYrGaP9TIm2DmY2U8mj9oedc/W2kfQDSf+mZOx5HifpITN7zsw+UaL9ECUzKf3KzGaY2Y1m1q2BfV6iPVN0+wdy7k1J/6VkDZKlktY75x6Kt1O69pGZ9TN/7SMArR5v0vZajjkl441Uccwh3rQzXOM0K65x6ityjdPs8YabKiCHJQut3q1krO2GuN05t8slC7UeKGmimR0Zvf9cSSucc5Ws1fEu59yxSiY5+JSZxdPIdlTySP/nzrnxSmYv+nxO3zsrWb3+rhJtfZT8wnWIkl8Ru5nZh0t8vvJrHwFoVg3FG6l2Y05evEnbG4w5xBugde3L1zgtEW+4qUJ7UslUsBUzs05Kgs2tzrl78rZNH1E/piSJ1HeSpPMsWVPoDknvMbP/K7OPN9P/XSHpXqneuh9LJC3xfimapCQAlXO2pOedc8tLtJ0haaFzbqVzboekeyS9s0y/bnLOHefKrX0E7JuqFm+kmow5efFGqjDmEG+AsrjGCRW+xmnueMNNFdqTZyWNNrND0l8wLlEyPWyjmZkpGdf7snOu5GruZjbAzHqn5a6S3qtkxpqMc+4LzrkDnXPD0/486pyr90TIzLqZWY89ZSULPc6O9rVM0htmdmj60ulKZu4p51KVGYqj5JH4CWZWl37W05WMqS71OQem/7tn7aPbco4J7CtaNd6k29VyzMmLN1KFMYd4A5TFNU6o8DVOc8ebai3+CzQ759xOM7tayXoeHSTd7JybE29nZrdLOlVSfzNbIumrzrmbos1OUrI+xovpeGJJ+qJz7k/eNkMk3WJmHZT8QHGnc+4P/5+9+463oyr3P/590nuvhJKeEEJCCpAA0kFEQFSuKEqRi40rTX8o14ad2MUCSLuoSG9G5aKG0LwmBNIrkAohvVdS1++PmTNZa2XvfU4yp+fzfr3Oi2ftZ/bs2YfsdWbtWc+agzz8rpKeTj77aiTpIefccwW2u07Sn9IOdaFU+MaGaad1jqTPFco75141syckTVFyQ8WpSu6zVMiTZtZRyX2c/qsChaNAvVcD/Y1US/uc8vob6YD6HPoboADOcfapxHOcSu1vzLn9FvIAAAAAAFQQ0/8AAAAAIAcGVQAAAACQA4MqAAAAAMiBQRUAAAAA5MCgCgAAAAByYFAFAAAAADkwqAIAAACAHBhUAQAAAEAODKoAAAAAIAcGVQAAAACQA4MqAAAAAMiBQRUAAAAA5MCgCgAAAAByYFAFAAAAADkwqAIAAACAHBrV9AEAB6pTp06uZ8+eNX0Y8EyePHmNc65zTR8HUNnob2on+hzUV/Wtz5k+Xdq9u3CuUSNp6NDqPZ6DUdH+hkEV6pyePXvq9ddfr+nDgMfMltT0MQBVgf6mdqLPQX1V3/ocs+K53buluvBWK9rfMP0PAAAEunVLToYK/XTrVtNHBwC1D4MqoIqUOinhxARAbbZy5cHlAOBQVaPT/2r7vNE9e/YE7YYNG2bxjh07gtzuaMKoedc7Lbr22bx588o6xEq3ePFirVmzpsTFWlRUeScenJhUr9re38TWrl2bxVu3bg1yzrmg7fdNzZo1C3KdOnWqgqOrPNTGoL6qa33OoYBzHFSlGh1U1cS80fhkJB7w+NavXx+027dvn8ULFiwIcmvWrAna/klO06ZNg9yxxx5bsYOtASNHjqzpQwCqRE30N3v37g3afv/j9xGF/OEPf8jiCRMmBLn4Sxy/bxo4cGCQu/rqq4u+xoH0h6WeeyDPi1Ebg/qqvtXG1Aec46AqMf0PAAAAAHJgUAUAAAAAOdT7JdVL1UVJ4RSWeJrerl27grZfC7V9+/Yg165du6LPbdy4cZD7zGc+E7R//OMfFzx2AHVbgwYV/95qxowZQfvKK6/M4tGjR5fcr9/H/OIXvyi6HynsA+NpewcypS/PlD8AAOobrlQBAAAAQA4MqgAAAAAgh3o//a+8FbYeffTRLP7Wt74V5OLpOI8//ngW33zzzUFu6tSpQXvcuHFZfPbZZwe5a6+9Nmj7K3k1ahT+L6msFbYA1Lx58+Zl8cpoTf0uXboE7VdffTWLb7311iC3cePGoO1PTb733nuD3Msvvxy0//Wvf2XxV7/61SDXpEmToscOAACK40oVAAAAAOTAoAoAAAAAcmBQBQAAAAA51PuaqvL4NUyHHXZYkPvGN74RtM8///wsfu6554LcokWLir7GHXfcEbR79uxZ4eOjjgqoOyZPnhy0n3nmmaC9bNmyLD755JOD3IYNG4J2hw4dsnjAgAFBbtWqVUHbr6kaOnRokNu5c2fQbtOmTRbHt3M47bTTsvjoo48Ocp06dRIAACiMK1UAAAAAkAODKgAAAADIgUEVAAAAAORQZ2uqSt2/ya8hmDJlSpCL6xbee++9LJ4/f36QmzVrVtB+9tlns7hdu3ZBrnv37kH7zTffLHrsb7zxRtDesWNHFsd1Xbt27crirl27BrkGDRgTAzXNv9fTWWedFeTiOiS/Nmrw4MFBbvHixUH7j3/8YxaPGDEiyPXv3z9o+33I2LFjg9z73//+oO3XSk2cODHI+ffXi3MXX3xx0O7Xr58AAECCs3IAAAAAyIFBFQAAAADkUGen/5VaanzOnDlZ/NprrwW5eGlifxrNcccdF+TefffdoL1ly5YsjpdKHjZsWNBes2ZNFm/fvj3ItWzZMmivXbs2i996660g16RJkyxu3LhxkGOJY6D6zZw5M2j70+1+9KMfBbn49gn+LRx69+5dctv169dn8ac//ekgt3DhwqC9bdu2LJ42bVqQO/HEE4tuG0837tGjR9H9/PznPw/ad955pwAAQIIrVQAAAACQA4MqAAAAAMiBQRUAAAAA5FBna6pK8WsR+vbtG+T8uihJ6ty5cxZv2rQpyHXs2DFo+zVMr7/+epCbNGlS0PaXS169enWQ27x5c9Bu37590df0l02Pa7MAVL/JkycH7eeeey6L77///iD35z//OWj7n29/aXNJmjdvXtD+y1/+ksVx3xQvv75y5cosjusy41sx+Ld0iGuzOnTokMWDBg0Kch/84AcFAAAK40oVAAAAAOTAoAoAAAAAcqgX0//iKX3+9Lru3bsHOX/5Y0k69thjs/i9994r+TqtWrXK4p07dwa5eGqev/z5nj17gly8HHyLFi0KxlK4/LEfA6gZ48ePD9q9evXK4vi2DG3btg3afh/iTxGWpCVLlgRtv+8688wzg9yCBQuC9q5du7I4XvI9vvWCP1Uwnhro7ye2dOnSoO3fNoLbOwAADnVcqQIAAACAHBhUAQAAAEAODKoAAAAAIId6UVO1YcOGoL1jx44s7tatW5Dz6wmkcLnzli1bBrmGDRsG7WbNmmVxmzZtgpxfQyVJzrksjpdJj+ss9u7dWzCWwtqtuN7Bf5+S1LRpUwGoWvHy5u+8804Wjxw5MsjFNZ1+3Wa7du2CnH8rCCnsU/r16xfkNm7cGLT9Wkx/yXRp/1s4+K/r92mSdNppp2Xxk08+GeTipdrXrl2bxdRUAQAOdVypAgAAAIAcGFQBAAAAQA4MqgAAAAAgh3pZU9WkSZMsjusW2rdvH7T9uqQ459dFSVKDBvvGoPE9rZo3bx60/VqFeNv4nlZ+PURc87V79+4sjt9LXNvRuXNnAahapWqhnn322SAXfyb9z35c77l48eKi7Tg3b968oN2hQ4csXrhwYZC75pprgvayZcuyeNq0aUHupZdeyuJ///vfQS6+h15c0wkAwKGMK1UAAAAAkAODKgAAAADIoV5M/4un0/nT/+Jl0eNt16xZk8XxVB1/up8kmVnRY2jUKPxV7tmzJ4vjZdLjpc/958ZLHMf79cXbAqh6I0aMCNpXXnllFsdT5uKpeOvWrcvi5cuXB7l4SfUtW7ZkcTzFOV4m3e9v/KXOJWnp0qVB218afdu2bUHO7w/j5eHjaY/+lEMAAA51XKkCAAAAgBwYVAEAAABADgyqAAAAACCHelFTFS9Z7tdRxXVH8ZLlfh1DXDMQ1ybs3Lkzi+Nap7h2y6/rimuz4hqrNm3aZLG/pLEkDRs2LIvjmq54yXcAVWPmzJlZ/PDDDwe5T3ziE1kcf7b9WyJIUtu2bbO4VatWRXNS2N/4sSTt2rWr6LF27Nix5H79vivum/x+67zzzgtyK1asCNovvPBCFl9++eVFjwdA1YnPU+I6Tr9u8u233w5ygwcPDtp33313Fsef6cMOOyxo+/1KfDuamN8vxn1OKfE5Tqm6dqA24EoVAAAAAOTAoAoAAAAAcmBQBQAAAAA51IuaqrjeoEWLFlkcz8ndtGlT0O7WrVsWx/eCiefv+nOB45qquJbCf27jxo2DXFxn4XviiSeCdv/+/bM4ntMc15IBqBpbt27N4ri26IEHHsjiZ599NsjdeuutQdv/PHft2jXIxXVS7777bhaPHj06yMV1CV26dMni+P5R/fr1K7ptfG+sD3/4w1k8d+7cIDd9+vSgPXz48CympgoorFjtc3n1Qf695+Ka7fHjx2fxr3/96yC3YMGCoO33XX7NpCT16dMnaPs156eddlqQ+81vfhO0x40bl8Vjx44NcqNGjQrapeqo/PO3+PiooUJdw5UqAAAAAMiBQRUAAAAA5FAvpv/t2LEjaPvLe8aX3ufNmxe0/SXXmzZtGuS2b98etP3L8bE451/ujqcGxksp+55++umg/eUvfzmL40vjW7ZsKbofAJVn0KBBWXzbbbcFuXPPPTeLO3fuHOSefPLJoO0vQ3z44YcHuXiKzEMPPZTFvXv3DnLxFJ/ly5dn8SuvvBLk4uWO33nnnSzevHmzijn//POD9hlnnBG0/d8JgPIdyNLi/pS/KVOmBLlf/vKXWTxgwIAgd+mllwbtESNGZHF825h4uvKECROy+J577glyrVu3Dtr+9GV/2rAk9erVK2jfcsstWXzRRRcFufi8BqjLuFIFAAAAADkwqAIAAACAHBhUAQAAAEAO9aKmKl52s02bNlkc11stXrw4aPvzhONt4yXL/aXR4/nQcTteAtXnL/kuhXVf/hLvUris8pAhQ4JcXKsFoGq89dZbWfzmm28GOf+zvmrVqiAX3z7Br72MazbjPsOvfZo9e3aQi2tD/b4rriONl2p/++23s3jdunVB7phjjsnieMl3/3cgSTNmzMjiuG8CkPDPT0qdF5Ti10VJ0tq1a7M4voXCgbjyyitLtn2LFi0K2t///vezeNq0aUEurvf261Dj/XTv3j2L4/4o7sv8/jM+/ynV75155plZvG3bNgFVhStVAAAAAJADgyoAAAAAyIFBFQAAAADkUGdrqvx6p/geUf79pjZt2lRyP/782pYtWwa5Ro3CX49fUxXXKcT8Woq45iu+L4NfN7Vs2bIgt3Tp0qKvQU0VUD38eiL/3nZS2Bc89thjQW7MmDFB269Ziu8ZE3+e/drLyy67LMhNnTo1aPvHFNcsfOADHwjao0ePzuK4huGmm24q+hpxLYLfH27YsCHIxe8NOBTt3Lkz+Bvu12PGtdXNmzcP2n6d9o033hjk/BrKf//730Eu/iz650rxeUt87jRp0qQsXrFiRZDza9UlaeDAgVl8zjnnBLl+/foFbf+efM8880yQ8++rF98Ly+9jpLCPjOtV4/7T/z0cf/zxWVzqfqNAXlypAgAAAIAcGFQBAAAAQA51dvpfqUu4/mXqePnjmH/J3Z82WOg1/GVC46VR40vP/qX7OBdftu7Ro0cWl7eMsS+eVugf78Eu3Qpgf5MnT87ieAljf3njN954I8jFU4jHjx+fxQMGDAhy8TLEL730UhYPGzYsyMX9mj/VxT8eSTr11FOD9oQJE7I4nop85JFHZnE8/c/vpyRpzZo1Wbx69eogx/Q/IPk73KpVq6ztT8Xzb20g7T/V3z+HOPbYY4PcfffdV/Q141vD+J/xuMShS5cuQftjH/tYFvfq1SvI+Uuf5/G5z30uaPslGnF/GZ8r+eIl1OO2z++P4imGQGXiShUAAAAA5MCgCgAAAAByYFAFAAAAADnU2Zoqnz/3WAqXKp0yZUrJ5/o1Vdu3bw9ycV2Sv7xneTVL/tzg+PhKzf31519L+9do+ErValFTBVQefxnyUaNGBblZs2Zl8SmnnBLk2rdvH7RnzpyZxTt37gxy8efZ7yfi+s54OWa/pineNq5L8JdVjmuq/P4nvh1FXNexefPmLI5rMwAkf4f9ep7zzz+/Bo+mdoqXagfqMq5UAQAAAEAODKoAAAAAIAcGVQAAAACQQ52tqfLrAuJaBP/+TevWrSu5H/+eBVu3bg1ycc2DX6sQ1yKUup9CXN8U1275tVodO3YMcv77jB1IrRaAgzdt2rQs7tu3b9FcfC+n5cuXB+133303i+P7vvg1SlJ4H5v4HjaLFi0K2n5+27ZtQW7lypVF9xv3N/3798/iuP87/PDDg/aSJUuyeP369UGubdu2AgDgUMKVKgAAAADIgUEVAAAAAORQZ6f/+VNT4ul//nLm8VS7mD9NZcWKFUEunl63ZcuWLN6xY0fJbf0piPEUvvh4/SVF42kz/lSdmP8+C+0XQOX461//msXxNNvbb789i9///vcHuREjRgRtv58YPnx4kHvnnXeC9gknnJDFxxxzTJCLP+t+vxFPRR46dGjQ9qdEx0u++0uzf+lLXwpy8e0d/KmMX/va14Jcz549BQDAoYQrVQAAAACQA4MqAAAAAMiBQRUAAAAA5FBna6r8uga/fikWL2ncr1+/oO0/N176PK5Z8ttxzl9uPT6+WFx/5Tv66KOD9rx584puS00VUD1++tOfZvHo0aODnF9r2adPnyC3YcOGoO3XVzZr1izItWvXLmh369Yti+Ol2uPP+rJly7J406ZNQS7uH4844ogsfu+994KcX6t6zTXXBLlTTjml6DHEOQAADjVcqQIAAACAHBhUAQAAAEAO9X76X7wk+eGHH150P/Ey6fGUPn959nj6TXwMfj7OlVrmvVWrVkWPIT6eeLpivJQygMqxcOHCLI6n7fmfywEDBgS5559/Pmg/9dRTWTxlypQg50/hk6QHHnggi9evXx/k4uXX586dm8XxlL54v9OmTcvitWvXBrlzzz03i/3l1SVp5cqVQdufKhhPc+zcubMAADiUcKUKAAAAAHJgUAUAAAAAOTCoAgAAAIAc6mxNlc9fpjgW1y/17ds3aPt1SU2bNg1ycS2Uv22c8+sLYnHtUyktW7YM2v7xb9u2LcjFS6qXOgYAB2/r1q1ZHNca+e2RI0cGueHDhwdt/5YO8TLk06dPD9p+7dbHP/7xIDd79uyirxPXXl522WVB2z/GdevWBbnzzjuv6Gv4S8dL4e+kVJ0oAACHAq5UAQAAAEAODKoAAAAAIAcGVQAAAACQQ52tqfLvxVKqZmnx4sVB+6STTgraixYtyuLly5cHuebNmwft9u3bZ3FcxxXXMfj3jIq3LVUDFr/mxo0bi75GXFMFoGps3rw5i+N7RM2fPz+LW7RoEeT+/ve/B23/MxzfV27FihVBe9CgQUWPJ36dY489Nov9e2pJUrt27YJ2ly5dsji+95TfB8b3zIvv+ef/TuJ7YwEAcKjhShUAAAAA5MCgCgAAAAByqLPzx/ypM/7Sw1I4xSaelhIveeycy+ImTZoU3Y8ULj8cT43Zu3dv0PaXG/anyUhSgwbhWNY/xngJ5m7dumVxPO1owIABQbvUtEIAB8+fXjdq1Kgg9+abb2Zx48aNg9ymTZuCtt/H+FN7JWnChAlBu1OnTlk8bty4IBcvb967d+8sfvXVV4PcOeecE7T9fiSeHt2/f/8sPu2004LcnDlzgnabNm2yDNaTwwAAIABJREFUuE+fPgIA4FDGlSoAAAAAyIFBFQAAAADkwKAKAAAAAHKoszVVZlYwlqRly5Zl8c6dO4PcJZdcUrUHVkDHjh0rvG1c8+XXTowfPz7I+XUeUljHBaDyHHnkkVn8/PPPBzl/qfG4XnLGjBlB+7DDDsvibdu2Bbm4vqlDhw5Fjydejn379u0FY2n/mk7/deM6Tb/GtGnTpkEuXn69R48eWezfbgIAgEMRV6oAAAAAIAcGVQAAAACQA4MqAAAAAMihztZULVmyJIvj+71s2LAhi7/5zW9W2zFVhRtuuCGLe/XqFeRWrFgRtP17ZVHjAFQev37x17/+dZCbNGlS0eddccUVQXvixIlZ3LBhwyAX33vKr8VcsGBBkIvvh+XXScW1WnGdl19nGvcTAwcOzOK4Hixu9+zZM4vjulYAAA41XKkCAAAAgBwYVAEAAABADnV2+l+rVq2yeNeuXUGuTZs2WXz66adXeJ/+csJS7ZjS8tGPfjSLmzRpEuT27NlT3YcDHJIaNdrXVX7kIx8Jct26dSv6vMGDB5ds+66++uqgPWLEiCyO+zh/aXYpnIrXvXv3IDdo0KCi21544YVFj8d/fWn/aYRHHHFEFteGvhIAgJrElSoAAAAAyIFBFQAAAADkwKAKAAAAAHKo0ZqqyZMnrzGzJeVviWp0VE0fAFAVqqO/ue2226py9/UVfQ7qJc5xaiX6G1SZGh1UOec61+TrA3XFLbfcUjI/ZsyYajqSuov+BkB1os8BDi11dvU/AACwT6kvX/jiBQCqFoMqIMKJCQAAAA6ExfdmAmo7M1stqdQ89U6S1lRgVxXZrrr3VVeP/SimuqA+qkB/I9W+z2NNvF5l7qsi29DnoF6qhec49b0vqch2FetvnHN14kdy3ST3iOQWSG6y5J6VXP+D2E87yV1bIn+D5GZJbrbkboxy10luXpr7cfrYyZKbIbnXJdfPe41/SK5Bidd5QnK907iV5H7nvbcXJXfiQf6evubFTST3suQa1fT/v+r9t6LXK2u76t5XXT72+vIjua+nn/EZkptW9lmU3GLJdSqw/UWSu6XIvk6X3ElFcu0l93T6OpMkN9jL3ZQewyzJPSy5Zunjf0q3/6G37Tckd3GJ9zNMcvelcVfJ/VVy0yU3R3LPesf51yLPv1dyg4rkbpRcC689TnLta/r/YfX/m6l9n8e63JfU1f6mWN9RCft9UXIjD2YbyX1RcvMl5/z+S3ImuV+luRmSG+7lrpTcW+nPleljTSX3XNonXette7f/3AKvf7HkvpXGA9LjnCa5uZK7u5J+P0X7r0LbSO4CyX23pv+9HPz7rZ2fx9q4r8p8vYr81Ikl1c1kkp6W9KJz6uOcRkj6b0ldD2J37SRdW+R1Bkv6jKQTJA2VdIGZ+qa5MyR9SNJQ53SMpJ+mT/uypPMl3Sjp8+lj35D0Q+e0t8jrHCOpoXNamD50r6R1kvql7+3TSkbNB+NrZYFz2inpeUmXHuS+gEOKmUZLukDScOc0RNLZkt4p9RznNNY57Tcv1EyNJJ0u6aQiT/2apGnp61wh6fb0eT0kXS9ppHMaLKmhpI+baYik7en2x5uprZm6SzrROT1T4hC/JulXafxdSf90TkOd0yBJpVdASd7fNc5pToH311BJv9fCe/iPKtK/AvXZwfQd1eT/lBxLfOXjA5L6pT+flXSnJJmpg6RbJZ2o5FzoVjO1l/R+Sf+SNETS5em2Q5Wcy0wp8fpfkXRHGv9K0i+c03HO6WhJv8797g7O3yRdaBb0XUBudWJQJekMSbuc011lDzin6c7pFTOZmX5ipllmmmmWDCDM1MpMz5tpSvr4h9KnjpHUx0zTzPST6HWOlvSqc9rmnHZLeknSR9LcFySNcU470tdflT6+S8lJRQtJu8zUR9IRzunFEu/nk5L+nB5nHyWd1zfKBmHOaZFz+lua/1L63maZ6cayHZjpGTNNNtNsM302fWyMpObpe/tTuukz6esBKF93SWu8z/ka57TMy1/n9SkDJclMV5npN2n8gJnuMtOrkh5T8kXLTeln8n3Raw2SND59nXmSepplXxQ1UvJZbqSkb1mmpK9pbqYGkhpL2qNkkHRrsTdjptaShjin6d77W1qWd04zvM1bmekJM80z05/SL7NkphfNNDKNt5jpZ2aaLunrkg6T9IKZXkj3MVbSJ4odD1CPFe07zPQtM72W/h2/O/ps/chMk8z0ZlkfYabmZnrETHPN9LSk5mUvYqY7zfR6+rf/O+UdlHOa6pwWF0h9SNIf0i/YJ0pql35J834lX7ysc07rJf1T0nnad67TWEqOX9L3JH2z2Gubqb+kHc5l06ri/mdmul1PM72S9q1TzJIvosx0evo7KtQvnZc+NkX7ztNkphPMNMFMU830bzMNKPA7cZJeVDIIBipNXRlUDZY0uUjuI5KOU3Jl6WxJP0k7hvckfdg5DVcyKPtZ+mG8RdKC9JuSm6N9zZL0PjN1TL/BOF/SEWmuf5p71Uwvmen49PHbJP1ByZWz30j6gZIrVaWc7L2fY5R8W70n3sgsu2p1oqRRkj5jpmFp+ur0qtZISdebqaNzukXJN9nHOZcNpGZJ2bEeKu6uxO2qe191+djrg39IOiI9wbnDTKdF+TVpn3KnpP9XZB+HSzrJOX1E0l3a983sK9F205WeDJjpBCX3TzncOb2r5Er425KWS9ronP7hnOZKWi1piqS/SOorqUE53xKPVNIHlPmtpPvM9IKZvm6mw7zcMCVXngZJ6q2kn4q1VPLF01Dn9F0lg70znNMZkpSehDU1U8cSx1Qf1cbPY13uS+pif1Oq7/iNczo+vfLcXOHJfCPndIKSz17ZFyRfkLQtvZpzq6QR3vZfd04jlVwxOi29gn0weii8krY0fazY4/+U1FPSREm/MtNFkqZEXzrFTpaC/ukXksab6X/NdJOZ2qWPr5J0Ttq3Xqp9V9alAv2SmZpJukfShUp+N9287edJep9zGibpW5J+WOTYXpf2+6Krrqitn8fauK/KfL1y1ZVBVSmnSHrYOe1xTiuVXF06Xsk3KT800wxJ45R0CiWnC6YnLT9S0jk+J2malA12GknqoGRwc7Okx8xkzmmacxqVnlT0VnISZGZ61EwPet88+7orOTmqyHt72jltdU5bJD2lfZ3A9em3xROVDPz6FXlPeyTtTL+xPiQ45yr04ajIdtW9r7p87PVB+jkboWQ6zGpJj5rpKm+Tp9L/TlZyglHI44W+JClgjJJvh6dJuk7SVEl70qk2H5LUS8mVoJZm+lR6fDemA7SfKf2WOB0cPWamzxR4jaCvcU5/V9JP3SNpoKSpZiorvp3knJamV8ynFXl/eyQ9Wc77WpUe9yGjNn4e63JfUhf7m3L6jjPSL2RnSjpTyZepZQr1KadKejDd7wwpuKL8sfTqzNR0P4Mq/c0U4Jx2O6fL0sHK40oGOj8z08/TK0kXFXha3P/8j5IZQY8rmRo90UxNlVz9uif9/Tyu8D0V6pcGSlrknN5Krzo96G3fVtLjZpqlZBDn/659dbafqq2fx9q4r8p8vYqoK4Oq2Qq/qamIT0rqLGmEczpO0kpJzcp7knO6zzmNcE6nSlov6c00tVTSU+ml8kmS9sqre0qvgn1DyYnOrUrmEd+jpDYitt07ltmShlpSn1AhZjpdyVW50c5pqJLOtdR7a6rkyh2AcqRf0LzonG6V9EVJH/XSO9L/7lHxW1JsreDrbHJOn077pyuU9FcLlXy2Fzmn1c5pl5KTrqAuy5LpzJMltZLUxzl9TNIltn+NgN/XlL3uOuf0kHO6XNJrSk7g/PdW6v29V4EBY7P0dYFDSqG+I72qcoekS5zTsUrOC/zPZEX6FEmSmXopuUJ+Vlq39TdV4LymiHe1byaOlFxhf7fE475rlczQGSVpo5KrS18u8BqF+p9lzul+5/QhSbuVzES6Sck52lAlV9ebeE+pSL/k+56kF9KrghfGr++hn0KlqyuDqvFKppR8tuwBMw1J5x+/IulSMzVMv3E9VdIkJd9WrHJOuyxZZOKo9KmbpeJXbczUJf3vkUqm5jyUpp5RMo2wbJ5wE4XLL14h6VnntE7JvOO96U+hQsi5SqbuyDktUHIZ+jveXOGeZvpg+t4uNlMLM7WU9OH0sbaS1junbZbUdYzy9r3LTI2999NRyZSlXcXeM4CEmQaYBVd9j1P5y2mXUrS/MVM7s+zk4RpJLzunTUqm/Y1KP/cm6SwlfUbZ8xor+Zb4x0qmEpXdF6OhwpMRyetr0ueeWTbwSq9e90lfr1LeX3q83aSCNRxAvVWi7yg7qV9jplaSLqnA7l6WdFm638FSNsWvjZIvbTams2A+kOOQx0q6wpK69FFKphkvl/R3SeeaqX161fzc9DGlx9NeyfTFP2jfuY6TV/flifuf88rOT8zUTVJHJQO2tpKWp1ejLpfK/ZK5rAa1T9r26zjbat8g8KoS++ivcGo0kFudGFSll3c/LOlsMy0w02wltUwrlKwKOENJfcJ4SV9xTisk/UnSyPRy8hVKPoRyTmsl/Z8lBaPxQhWS9KSZ5iipWfgv57Qhffx+Sb3TS8qPSLoyPS6lJylXKalXkKSfS3pW0i+lfYtreP6m5NJ3mWuUTE2cn+7/ASUDwilpPEnSq5LudU5TlUxNbGSmuUqmEE309nW3pBm2b6GKM9LXOySY2Xlm9oaZzTezgiubmdn9ZrbKzIp2qGZ2hJm9YGZzzGy2md1QYJtmZjbJzKan2xQtGjazhmY21cz+WmKbxWY208ymmdnrRbZpZ2ZPmNk8M5trZqMLbDMg3UfZzyYzu7HAdjelxz3LzB42s4Lf6JnZDek2swvtp55pJen3ZpqTTh0eJOnbOfb3F0kftsILVRwtaZaZ3lBycnSDJDmnVyU9oaQWYaaSftqfmvBfkn7vnLYp6ftapP3cZK+/UrqveZLaetN/R0h6PX1vE5T0Ka/leH93S3rO9i1UMULSRJcs9FPvVWd/k25X6/qcivY36bbl9jl1uL8p2Hekn8l7lJzA/12q0OftTiULx8xVshjNZClZoEvJzJR5Sr7w/b/ydmSm6820VMkVpxlmujdNPavkyvj89PiuTV9jnZKrPa+lP99NHyvzLUk/SAdAf1dSkjBTycqfsZclDSv7wljJAG1WWrrwd0k3p+drd0i6Mn18oMq52u+c3lMyzfJv6VTIVV76x5JuM9NUlb6qVSfPjTjHqdxznErvbypjXXZ+DuxHcs0lN1FyDavhtZ7SQdzPqy7+KPl2a4GSmpEmSgba+91fR8nVzOGSZpXYV3dJw9O4tZJpoIOibUxSqzRurGTgO6rI/r6k5I9g0XtpKPl2f7/7IEXb/F7SNWncRFK7CvxOVii5cZ3/eA9JiyQ1T9uPSbqqwPMHKzkZaKHkD9Q4SX1r+v81PxX/UXLPq2uq6bVul9xZNf2eq+e9Vm9/k+ZqdZ9TrL9Jc+X2OfQ39e8n7RPOrunjiI6pq+Ser+njOPDj5hynyO/koM5xqqK/qRNXquob57RdSd1Vj6p8nXRq0TPOZXVh9d0JkuY75xY653YquaL4oXgj59zLUvDN236cc8udc1PSeLOSaQw9om2cc25L2myc/jhFzOxwSR+Usm8ID4qZtVXSWd6Xvv5O59yG0s/SWZIWOOcKTWFLl+02f9nuWHqbAbfNORffZgB1w50K6xKq0izn9Hw1vVZNq9b+Js3V9j6nVH8jld/n0N/UPz9U4TKImnSkCteA1Xac4+wvzzlOpfc3DKpqiHP6u3O5ahkq8ho7ndMfqvI1apliS8HmYmY9lSzr+mqBXEMzm6Zk+sE/nXP7baNkGuhXpMI3g/Y4Sf8ws8lm9tkC+V5KVlL6n/Qy+71m1rKcfX5c0sP7vZBzBZbtdv8o8Pz0NgPW0czi2wygDnBO7zlXcGpOVbzWPdXxOrVEtfc3ab429zkF+xupwn0O/U0945xWOqexNX0cPuf0mnOaVtPHcRA4x9lfnnOcSu9vGFQBJZhZKyVLSN/onNsU551ze5xzxymZr36CmQ2Onn+BpFXOuWL3WfOd4pwbrqS+5r/M7NQo30jJJf07nXPDlMw7LzinOn3tJpIuUrJEbZwrsGy3farA+yt1mwEAlai8/kaqvX1Oqf4mzZfb59DfANXrUD7HqYr+hkEV6pOKLAVbYWbWWEln8yfn3FOltk0vUb+g5M7zvpMlXWRmi5Vcqj/TzB5UAek3K3LOrVKyAMsJ0SZLJS31vil6QkkHVMwHJE1xzq0skEuX7XarnXMFl+32jus+59wI51x8mwHgUFZj/Y1UK/ucUv2NVME+h/4GKIpznFDuc5zK7m8YVKE+eU1SPzPrlX6D8XHp4KYdmJkpmdc71zn38yLbdDazdmncXNI5SleZLOOc+2/n3OHOuZ7p8Yx3zu13RcjMWppZ67JY6SpJ0b5WSHrHzAakD50laU6Jt/EJFZmKo2zZbmuRvtdg2e7o2NLbDFh8mwHgUFat/U26XW3uc0r1N1IF+xz6G6AoznFCuc9xKru/Ke8makCd4ZzbbWZfVLJUa0NJ9zvnZsfbmdnDSpa072RmSyXd6py7L9rsZCX3y5iZzieWpK855571tuku6fdm1lDJFxSPOeeKLidajq6Snk4++2ok6SHn3HMFtrtO0p/SDnWhpE8X2lnaaZ0j6XOF8s65V82sbNnu3UqW6S12R/EnzayjpF2S/qsChaNAvVcD/Y1US/uc8vob6YD6HPoboADOcfapxHOcSu1vzLn9FvIAAAAAAFQQ0/8AAAAAIAcGVQAAAACQA4MqAAAAAMiBQRUAAAAA5MCgCgAAAAByYFAFAAAAADkwqAIAAACAHBhUAQAAAEAODKoAAAAAIAcGVQAAAACQA4MqAAAAAMiBQRUAAAAA5MCgCgAAAAByYFAFAAAAADk0qukDAA5Up06dXM+ePWv6MGql6dOl3bsL5xo1koYOrZrXnTx58hrnXOeq2TtQc+hvaif6HNRX9Dm1T0X7GwZVqHN69uyp119/vaYPo1YyK57bvVuqql+bmS2pmj0DNYv+pnaiz0F9RZ9T+1S0v2H6HwAAAADkwKAKAAAAAHJgUAUAAAAAOdRoTRXFeLXP4sWLtWbNmhKVOUDdVNv7m3feeSdob9++PYs7dOgQ5Pbu3Ru0zSumW79+fZDr2rVr0G7btm2u46xsLDiA+qq29zmHIs5xUJVqdFBFMV7tM3LkyJo+BKBK1Pb+5oYbbgjaM2fOzOLLL788yG3ZsiVoN2q0ryt/6qmnSu73ggsuqNDxxAO3Bg2qZmIDCw6gvqrtfc6hiHMcVCWm/wEAAACodN26JSsTF/rp1q2mj65ysaQ6AFSTF198MYvvuOOOINe0adOgvW7duiy+/vrrg1zDhg2DdosWLbJ41KhRQe6xxx4L2mPHjs3iMWPGBDl/mmFVXZkCABw6Vq48uFxdxF9NAAAAAMiBQRUAAAAA5MD0PwCoJG+88UbQ/tGPfhS033zzzSweMmRIkJs7d27Qbt68eRZ36tQpyK1ZsyZoDx48OIvj1f/8RSykcJrhjTfeGOT69u2bxZ///OeDXJcuXQQAAArjShUAAAAA5MCgCgAAAAByYFAFAAAAADlQUwUA5dizZ08Wx8uZ33nnnVk8ceLEINeyZcugfcIJJ2Rxq1atgtx7770XtOfNm5fFfn2VtH99k398r732WpD7z//8z6Ddvn37LN60aVOQW758eRZ/7nOfC3J33XVX0O7atWsWV9eNggEAqK34ywcAAAAAOTCoAgCgHujWTTIr/NOtW00fHQDUbwyqAACoB1auPLgcACA/aqoAoBxxHZVv5syZWdwtuhwQP8+/Z1R8P6mLLrooaM+ZMyeL/VonSfrZz34WtL/73e9m8bnnnlvyGPzarRYtWgS5Nm3aZHFcJ/XQQw8F7ZtuuimLqaECABzq+EsIAAAAADkwqAIAAACAHJj+BwAHIJ6250+n69y5c8ltd+/encWtW7cOcqtXrw7ap59+ehavjApiHnvssaDdq1evLB44cGCQ27p1a9DeuXNnFu/atSvI+Uu3x1MZly5dGrRLLTMPAMChhitVAAAAAJADgyoAAAAAyIFBFQAAAADkQE0VAByARYsWFc359VWStGPHjqDt1x61atUqyL399ttBe9OmTVncvXv3IOfXUEnSihUrsnjx4sVBLq7d6tq1axabWZDz66Q2b94c5OL3tnHjxizu0KGDAAA4lHGlCgAAAAByYFAFAAAAADkw/Q8ADsC7774btP1pcf40PGn/Zcn9KX1z584Nchs2bAjay5cvz2J/qfNC206dOjWLO3XqFOTiJdbfeeedLPan+0nSli1bih57bN68eVl80kknldwWAID6jitVAAAAAJADgyoAAAAAyIFBFQAAAADkQE2VxzlXst2gQeWMQV9++eWgfeqpp1bKfg/E1q1bs7hly5bV/vpAXRXXVDVt2jSL/c+VJO3evTtod+zYMYuXLFkS5NavXx+0mzVrVvA1JKlLly5B++ijj87ixo0bF92PFNaA9e/fP8iNGzcui+Ml3+NardmzZ2cxNVXAocU/P/LrPyXpsMMOC9p+v/jzn/88yH3xi1/M4vhcpEmTJkVfP64H9W9XAdQUrlQBAAAAQA4MqgAAAAAgBwZVAAAAAJADNVUeMyvZLuX6668P2m+//XYWv+997wtyzz//fNDu1atXFh9xxBEVfs24XqNRo+L/O3/yk58E7ccffzyLx48fn8V79+6t8OsDhyK/JkkK7+00f/78ILd9+/ag3bNnzyz266uk/Wuh1q5dm8VxvdW2bduC9ubNm7O4d+/eJffr1x5s3LgxyE2YMCGLBw8eHOTOPffcoB2/VwB1m18nFZ//LFy4MGjfeOONWfz5z38+yE2ZMiVo33DDDVn86KOPBrm//e1vWfzQQw8FuQsuuCBo+7VbLVq0CHKf/exng7bfv8b18UBV4UoVAAAAAOTAoAoAAAAAcqj30//i6Wx5pvj5l7+PP/74IHfZZZcF7eHDh2dxvNRnPO3nuuuuy+JnnnmmwsdTarrfH//4x6D9yCOPBG1/ytK8efOyOJ7aBCC0adOmoO0vF+x/rqRwam+c79OnT5CLl02fNGlSFq9evTrIDRo0qOh+d+3aFeTiKYj+tJn4+O67774s/vrXvx7k4imH8fLxAOq2UudD8bTisWPHFt32qaeeCtrnnHNOFvu3YpCkHTt2ZHFc/vDSSy8F7fj2EL5S50NAdeFKFQAAAADkwKAKAAAAAHJgUAUAAAAAOdSZSajxkpj+3N9SuQYNSo8bd+7cmcUrVqwIcsOGDQva/hKiX/3qV4PckCFDgvbixYuzOK49OProo4P2uHHjsrh9+/ZB7mtf+1oWX3zxxUEuXir5X//6VxbfcccdJbcdOnRoFvfo0aPodgBC/mdbCmuh4vrJT37yk0F7zJgxWRx/1uK+yq/d8pdXl6RVq1YF7enTp2dx3Bc1adIkaPu3YvCXYpfCJd/jJYvjejGWKQYOHf6tVyRpwYIFWXzkkUcGuQceeCBo++c8cd14y5Ytsziu6fKXUJekU045peDrS9Jf/vKXoP2pT30qi/fs2ZPF9FuoSlypAgAAAIAcGFQBAAAAQA4MqgAAAAAghzpTU1Xq/gmlcq+88krJ/d56661Z7NcWSeE9W6TwnldLly4Ncv49ZWLxfWLiOb0f/OAHs7ht27ZB7s4778zi+++/P8i1bt06aK9ZsyaL4znOo0ePDtqvvvpqFvu1G/7cYwD7i+f5d+rUKYs3bNgQ5OLPfr9+/bLYr22SwvvFSWG9Z9wvxHVdy5Yty+KTTz45yMXPXbJkSRbHfYh/L7643iq+R4xfYxXfwyquxwIOVcVqeOLzlng7/3wjrtUsJe5X/PvWlbcfv/7ytttuC3J+3yCF/UG3bt2C3O9+97ug7d/XM+4bzjzzzCzu0KFDkPPrxKXwfn1xbfqTTz4ZtP2aKv8eVgdyb1LgQHGlCgAAAAByYFAFAAAAADnUmel/pcyfPz9o+1NwHn744SAXT7H55je/mcXx0ufxEut+Pr7EHk+N8afR+ZfxJem9994L2jt27Mji//iP/whyF110URa/8cYbQS5eUvSII47I4rPPPjvIxVOAHn300Sz2L/lzaRzYnz8Vz4+lcCn0eGpLqWXJ46mCRx11VND28/ES6vHy5v7tH+L+Jd7Wfx1/6q8ktWrVKovjqTj+9GIpnPIT95W9e/cWgIr/TS21XallwOPn+VPdCrVL8ZdCj6cYH3vssUHbPx/q2LFjkOvevXvQ9sslrr322iC3cuXKLB44cGCQi89j2rRpk8VXX311kIv7yAcffDCL/amAQFXiShUAAAAA5MCgCgAAAAByYFAFAAAAADnUaE3Vjh079NZbb2XtRx55JIu7dOkSbBvXCfjL/fpLhkphLcAZZ5wR5PylPaVwKXS/NkIK5+9K4XKk8dLj69atC9p+jUG8NHG8zLJfUxXn/BqHAQMGBLlTTjklaLdv377o8TzzzDNB258DPXv27CyOf88AwrrNpk2bBjm//9m4cWOQi2sL/PqGuC6zefPmQdvf19q1a4Nc3K+9+eabWRzXPsX8Oq94iWX/mOLl1uO2f0xxHwcgUaoeqpQDWUbdF/crd911VxZPnTo1yPm3g5Ckq666Kov9pc4l6aGHHgrac+bMyeL4HOykk04qeny//e1vg/ZNN91U9PjiczD/dhHxbWPi9uuvv170GICqwpUqAAAAAMiBQRUAAAAA5MCgCgAAAAByqNGaqlWrVunOO+/M2tOnT8/iuG4h5tcmxPdgWr16dRbHNQ5xrVbLli2zeNGiRUFu1qxZQdu/10J8j5m4FsqvTYrrr2L+e43rKkaOHJnFr732WpD7zW9+E7T9mrBjjjkmyMX3svC37du3b8FjAZDwP++laqqGDBkS5Px7OUlhH+LXS0oYqLqYAAAgAElEQVT730/Kf8348xvv169Njesb4poO/95Ucf1F586dsziuMS1V7xn3swASB3vvR/+8Ia6v8muk/T5F2r8uye9XrrzyyiD30ksvBe2jjz46ixcuXBjk/PMqKTzHic+rSol/H/79peJzpW3btgVt/z5a5557bpCL+1O/xurtt9/O4vg+g0Bl4koVAAAAAOTAoAoAAAAAcqjR6X/t27fXJZdckrX9pdDfeeedYNv169cHbX8J32XLlgU5fzrg4sWLi+akcMrf1q1bg5w/xVAKp8PE+/GXKZakY489Novj5Y/j5ZGfeuqpLP7HP/6hioqXMY4vlfv8aY6S1KRJkyz2pwfE7xlAOEWl1FLo8bS8eCqeP2Wma9euQc6/tYIUfhbjbcePHx+0/eWNe/fuHeT8Wy3ExxBPN/anGfl9hLT/tB3/vcVTAwHsr9Ty6nv37g3apZZUnzZtWhbHt0Fp3Lhx0L755puzeNiwYUEuPi+YO3duFvtTgaX9pxX67+XBBx8Mcp///OeLHnvM70eWLFkS5Pr37x+0/enVTz/9dJC7/PLLg/Zxxx2XxTNnzsxibhuDqsSVKgAAAADIgUEVAAAAAOTAoAoAAAAAcqjRAprmzZsHS38fddRRWdy9e/eSz/WX3oznyPpLgfq1EJL0v//7v0H7qquuyuJ4OeSOHTsG7bjGoLJceOGFWfzcc88FuaFDh2ZxvMRxPOfaX1I0nrsd14stX748i/1arLgGBIC0Zs2aLG7dunWQ8z8zvXr1CnJ+jYIU1i/GNVRxPZZfVxrXLPn1p1JYGxXXScR1U35NWKlbKMR9Qbyt38dQpwAU5n9OSt1eJa5n9m99sGDBgiDn1x759dtSWF8pSV/96lez+LHHHiv6GpJ0xBFHZHF8/vPCCy8E7eOPPz6L4/OsuObzzDPPVDH+Oc7KlSuD3KWXXhq0/XOlD3zgA0HusssuC9p+zbnfP8W1a0Bl4koVAAAAAOTAoAoAAAAAcmBQBQAAAAA51GhNVcOGDYP7Pfnze59//vlg2/jeMP69GNq1axfkBg8enMXxvZu++MUvBm3/ni47d+4Mcn4dhbT/faF8cc2D347rm+K50z169MjiuG7hlVdeyWJ/7rG0f82DX2MVz92Ofw9+bUc8dxpAyP88N2vWrGiuU6dOQS6uNfD7O78GUpI2bNgQtP1+Iq6JjOuv1q1bl8VxfdOKFSuCtt9flurT4j43bvvHF/edABL+/d0O5D6Q/nnDn//85yD3xhtvZHH8effvYSVJs2bNyuL4HpmrV68O2mPHjs3iG2+8Mci9+OKLQfs73/lOFsd9zPe+972g7ddUbdy4Mch16dJFxcT79cXHF/Pvq+XXy8f9N1CZuFIFAAAAADkwqAIAAACAHGp0+l/MX87TjwuZP39+FsdT7956660sjqfUxMuS+5fR4yWO27RpE7T9KYf+JX1p/6k8/pLH8TS9eDqgf4m7c+fORV8zXgo03s/69etVTLwMtH98ffr0yeJ4+WUAofjz7E+Li6fIzZ49O2j7/U/cF8V9ld/HtG/fvuQx+P1E/BmOpxT704TjaXt+X+RPjS7En8oUTy8GkEzbnTBhQta+6667stifgi/tPzXQ7x/inP/3PJ5iHJdD+LdPmThxYpCLbzETnwP54inH8TQ+XzzN8MQTT8zi+HztnHPOyeK4n3vkkUeC9g033JDF/fr1C3LDhw8P2v6y87fffnsWx+dNQGXiShUAAAAA5MCgCgAAAAByYFAFAAAAADnUqpqqA9G3b98KbXfsscdW8ZEAqO/8+ia/fkkK6ynnzp0b5E466aSgPXDgwCyOlwv2a52kcLnjuKYivmWC347rreLaB7+moEmTJkHOr9ssdcsGKVyaOK4lA5DUN/rLeV9zzTVZHC9nHtdE+7VHcR2Sv4x6nIs/p9/4xjeyOK7LjuvG/dur+EuSS/vXan35y1/O4rgWvFT91Q9+8IMgt3Tp0izu3r17kIvrn/x8XDvasmXLoO33tX7/RE0VqhJXqgAAAAAgBwZVAAAAAJBDnZ3+BwDVxZ8yEk/F86cG+rcqkKQvfOELQXvhwoVZPGXKlCAXT6GZOXNmFs+ZMyfIxa/jT/+Lp97E0xWXLVuWxVdccUWQGzVqVBbH0wb944nFy8MDSD4X/rS0973vfTV4NLVDvIx7dWvRokWNvj7qN/4SAgAAAEAODKoAAAAAIAcGVQAAAACQAzVVAFAOv24q5tcznXLKKSX307t374JxIaeddlrRXLw08o4dO7I4Xmr4YMU1XqV+B/HxAABwqOFKFQAAAADkwKAKAAAAAHJgUAUAAAAAOVBTBQDlaNq0aRaXqi2K7wkV8+uvGjZsGOT8e2GV9zrxfaEOto6q1Gu2bt06yMXH69dR7dy586BeHwCA+oIrVQAAAACQA4MqAAAAAMiB6X8AUI41a9Zk8a5du4KcPy2uUaOD71Lj6X7+1LxSUwHziJdC999LPP3PX7Y9zpc37REAgPqOK1UAAAAAkAODKgAAAADIgUEVAAAAAORATRUAlMNfCj2uH9q9e3cWd+/evdJe80DqqErVX5VaNr1UTVW8THtcS+a/77j+ChV3yy23FM2NGTOmGo8EAJAHV6oAAAAAIAcGVQAAAACQA4MqAAAAAMiBmioAKEeDBvu+f9q8eXOQ27BhQxb7tVeF+DVMfv1SXqXqrw72HlfxPbfi9+bXlrVs2fKgXgMAgPqCK1UAAAAAkAODKgAAAADIgel/AFCOT3/601k8efLkIOdP/xsxYkTJ/cRT6mqaP60xFi8PH7f999KuXbvKPTAAAOoYrlQBAAAAQA4MqgAAAAAgh9o1FwUAAAAAqsgtt9xSMj9mzJiD2m+NDqomT568xsyW1OQxYD9H1fQBAFWhOvqbyy+/vCp3X2v9+Mc/zvN0+hzUS5zj1Er0N6gyNTqocs51rsnXB3DooL8BUJ3oc4BDCzVVAAAAAJADgyoAAAAAyIGFKgAAQKBUIffBFnEDwMGqqsUlKpM552r6GIADYmarJZUq/u0kaU0FdlWR7ap7X3X12I+ifgD1UQX6G6n2fR5r4vUqc18V2YY+B/VSLTzHqe99SUW2q1h/45yr9B/JfV1ysyU3Q3LTJHdiJe//dMn9tRL3d7/kVkluVvR4B8n9U3Jvpf9tnz5ukvuV5Oan73F4+vgAyU1OHxudPtZIcuMk16LE6/9Scqem8QWSmyq56ZKbI7nPVcH/ny05nz+u7HdRG38kvV5Z21X3vurysdenH8l1k9wjkluQfqaflVz/g9hPO8ldWyJ/U9pXzpLcw5Jrlj7+gOQWpf3nNMkdlz7+0XT7VyTXMX2sj+QeLfEaJrnxkmtTle9Ncp0l91xN/7+rmX8vte/zWJf7kvrQ31TVeZDkXpTcyIPZRnJnSm5K2t/8XnKNovzxktstuUvSdq09p5HctyX3/yq6jeR+Krkza/rfReW899r5eayN+6rM16vIT6XXVJlptKQLJA13TkMknS3pncp+nYNlVnDK4wOSzivw+C2SnndO/SQ9n7Yl6QOS+qU/n5V0Z/r45yTdIOl8Sf8vfewLkh50TtuKHE9HSaOc08tmaizpbkkXOqehkoZJevFA3l9VMpOZqYGkP0q6tqaPB6gKZjJJT0t60Tn1cU4jJP23pK4Hsbt2KvJZMVMPSddLGumcBktqKOnj3iY3O6fj0p9p6WPXSTpe0u8kXZY+9n1J3yhxDOdLmu6cNlXle3NOqyUtN9PJB7EvoN6ojedB6d/u30v6eNrfLJF0pZdvKOlHkv7hPa0+ndP8WvvO4YAqURULVXSXtMY57ZAk57TGOS2TJDMtNtN3zDTFTDPNNDB9vKWZ7jfTJDNNNdOH0sd7mumVdPspZjopfjEzHZ8+p0+J/VxlprFmGq9kcBRwTi9LWlfgvXxISSek9L8Xe4//IR2YTpTUzkzdJe2S1CL92WWmdpIulPSHEr+vj0p6Lo1bK6lzW5se1w7n9Eb6Hh4w06/M9G8zLTTTJd7v4GYzvWamGWb6jvf4M2aabKbZZvpsgd9dJzNNMNMHi+0n/X/whpn+IGmWpCMkjZX0iRLvCajLzpC0yzndVfaAc5runF5Jv1j4iZlmpX3YpZJkplZmet7r2z6UPnWMpD5mmmamnxR4rUaSmqdf9rSQkr6yhL2SmmpfH/M+SSuc01slnvNJSX+upvf2TPp6wKGs1HnQt9K/s7PMdHf6RYfM9KKZfpSev7yZfrZlpuZmesRMc830tKTmZS9ipjvN9Hr6N/47+x9GoKOknc7pzbT9TyXnH2Wuk/SkpFXeY1V5TnOhmV5Nz9XGmSVf7Jjp2+l53Ivpuc713vv9evq7+ZekAd7jn0l/p9PN9KSZWsQH5ZyWSOpopm7l/J6Ag1f5lyVdq/RS95uSu0Nyp3m5xZK7Lo2vldy9afxDyX0qjdulz20puRbedJh+kns9jU+X3F8ld1J6afrIcvZzleSWSq5DiePuqf2n/23wYitrp699ipd7XnIjJXdketl9guSGSO5nkju9nN/X7yV3ode+V8lUxIcl90nJNUgff0Byj0uugeQGSW5++vi5krs7Pb4G6bGVXXbvkP63eXq5v2y60BbJdZXcq5I7p9R+0t/LXsmNio77rbL91bYfSZ+trO2qe191+djry4/krpfcL4rkPqpkKnDD9DP0tuS6p1NiyqbXdVIyNdgK9SvR/m5IP4+rJfcn7/EHJPeGkmk3v5Bc0/Txc9I+7y+Sayu5f5Tq19LnLJFc6+p4b5LrIbmZNf3/sPr/zdS+z2Nd7kvqen+j0udBHbz4j2V//9Nzh5+l8fmSG5fGX5Lc/Wk8RMn0vJH+vtLP7IuSG+Lta2R0TJb2BWXPvb3ss5p+bl9K//Y/oH3T/6rynKa95NK6fneN996/Lbl/S65p2t+slVxjyY2Q3Ewl54Vt0n6obGpfR+/1vq9955nfljdFUHL3SO6jNf3vI/+/r9r5eayN+6rM16vIT6VfqXJOWySNUDItbrWkR810lbfJU+l/J0vqmcbnSrrFTNOUXBpuJulISY0l3WOmmZIelzTI28/R2ndZ+e1y9iNJ/3Su4NWoir4vJ8mVs83bzul05zRa0jZJh0uaa6Y/mulRM/Uv8LTuSn5PZfu4RtJZkiYpudx+v7ftM85pr3Oao33Tdc5Nf6ZKmiJpoJJpiZJ0vZmmS5qo5ApT2eONlVyx+4pz+mcF9rPEJVfkfKskHVbq91FTnHN3V9Z21b2vunzsh4hTJD3snPY4p5WSXlIyHc8k/dBMMySNk9RD5UypM1N7JVe9eyn5LLU006fS9H8r+QweL6mDpK9KknP6p3Ma4ZwuTJ/7rKT+ZnrCTPcU+oZWUgfntLma3lut7ReqUm38PNblvqSu9zflnAedkV6hmSnpTEnHeE8tdH50qqQH0/3OkDTD2/5jZpqi5O/2MQrPkeJjckqmF//CTJMkbZa0J03/UtJXndPe6DlVeU5zuKS/p7+Hm6Pfw99cclVrjZI+pauk90l62jltc06blMyYKTPYkllNM5VcKff35asX/VNt/TzWxn1V5utVRJUsqe6c9igZ1LyY/iO/UkndkqTkcriSD3PZ65ukj7r0snAZM31b0kpJQ5VMVXzPSy9XMmgapn1TZort50RJWw/iraw0U3fntDyd3ld2WfxdJYOUMoenj/l+oKTO4XpJ90paLOmH2n9qzPb0fWSc00xJM830R0mLpKwz3uFtZt5/b3NOv/P3YabTlczjHu2ctpllg0xJ2q2k036/khOnUvvpqcK/u2bpsQP1zWxp3/TaCvqkpM6SRjinXWZarOhzXcDZkha5pBZJZnpK0klK6hWWp9vsMNP/aF89g9JtWyjpF94v6a+SPpIe8ycl3RO9zm4zNUhPmKr6vdEvACp8HmSmRyTdoaSO8p30HMf/LBU6PyrITL2U9AvHO6f1ZnpA5fQ5zmmClE0rPFfKBkUjJT1iyVlFJ0nnm2m3c3rGe3pln9P8WtLPndPY9Hzl295T/HOdcn8XSs4vL3ZO09PB6+lFtqN/QpWqioUqBphlVzgk6TiVvxzt3yVd580tHpY+3lbS8vRk4HIlhdxlNkj6oKTb0g9kqf0crLHaV8h5pfbVJYyVdEVagzBK0kbvJEhmOk3SMpfUObRQUgexN41jcyX1TZ/XynsvUsV/d1ebqVW6jx5m6qLkd7c+HVANlDTKe46TdLWkgWbJN+Al9rOf9PfbTUmnCtQ34yU1Na8O0UxD0hqHVyRdaqaGZuqs5FvkSUo+b6vSQccZko5Kn7pZSV1BIW9LGmWmFuln6iwl/YHSL3HKPmsXK6ln9N0s6VfOaZeSGgun4n3MG5J6V9N761/gWIFDSonzoLLBxpr0b21FvuB4WemiNGYaLGlI+ngbJV94bkzrkT5QgePqkv63qZKr33dJknPq5Zx6Oqeekp6QdK0/oKqic5q22vdl9JUq38uSLrakxqy1ktquMq2VLJLTWKVrOumfUKWqYqGKVpJ+b6Y56XSRQQq/gSjke0qmpM0w0+y0LSXf6FyZTmEbqOiKSTpF5QJJv02vRhXbT0lmeljSBEkDzLTUTP+ZpsZIOsdMbyn5VrnszmLPSlooab6Sb4Wv9fZlSr7NKXvtuyXdLulvkn5a4OX/pn3fqpikr1iyMMQ0Sd+RgqmT+3FO/5D0kKQJ6bdhTyjpYJ6T1MhMc9Pjnhg9b4+SxSbONNO1JfZTyAhJE53T7lLHVhPM7Dwze8PM5ptZwZV+zOx+M1tlZkU7VzM7wsxeMLM5ZjbbzG4osE0zM5tkZtPTbYoWCptZQzObamZ/LbHNYjObaWbTzOz1Itu0M7MnzGyemc01s9EFthmQ7qPsZ5OZ3Vhgu5vS455lZg+bWcFvOc3shnSb2YX2U9+k02Q+LOlsMy1I+5LbJK1QsnLeDEnTlQxQvuKcVkj6k6SR6WfnCknz0n2tlfR/lhSl/yR6nVeVfM6mSJqppD8um4Lwp3RfM5V8c/z9sueZ6TBJJ3gnPb+W9Jqkzyv5DMeyPqYa3tsZ6esdEqqzv0m3q3V9TkX7m3TbcvucetLfFDwPck4blJwzzFLyReZrFdjXnZJapX/Lv6tklomc03Ql0/7mKfnc/18F9nVzup8Zkv7inMaX94QqPKf5tqTHzTRZFbiPkHOaIulRJf3T/yr83X1T0qtKfgfziryPxkoGewX/ndcVnONU7jlOpfc3lVGYxU++H8n9S3Ltavo4DuB4b5fcWTV9HPsflxpKWqDkW/kmSjrfQQW2O1XScEklFhBQd0np/cfUWtKb8b6U/MFolcaNlXTqo4rs70tK/vAVvb+akit/ncp5j7+XdE0aN5FU8t9N+jtZoeTGdf7jPZRMw2ieth+TdFWB5w9WcgLQQskUjHGS+tb0/2t+Kv6jZLGJf1bTa72sWnwPu8p9r9Xb36S5Wt3nFOtv0ly5fQ79Tf34qY3nNJL7sOS+V9PHke89cI5T5HdyUOc4VdHfVMWVKhy4L2vfghp1wSzn9l+avhY4QdJ859xC59xOSY9I2fLPGedcsSX0/W2WO+empPFmJVMaekTbOOfclrTZOP3ZbzETMztcyVTVew/4HYX7aauks7wvff2dzrkN5TztLEkLnHOFppGmy3lbqeW8j5b0qnNum3Nut5IavI/8//buPN6uqrz/+OfJvRluJjKSxAAJ82CACIj6U7CoCFYrpfXVglIqtlVbUMFW64BlEAWL4E9ppchcQYSiKEOsoIIgSoCEkAECJBggIQMBApknnv6x1z1Ze91z9rnJvveec2++79frvrLWWevsvfZNzpO9z37W2jt6DNLzPEtNvtKM4d25n5AyeKk7r3bnfppIj8ab0NbsMaco3kD9mKN40zc04zlNK3BJowdRks5xOipzjtPl8UYXVU3AnenuuRV9mpp7h4nwzWIi+QcsLqbKicn2MrPJZAuiTK/S1mJms8gWMbnH3Tv0IVtZ6YuQX1mpCgfuNrMZZtbhuWJkq8S9BFwbbrNfZWZD6mzzJOCmDjtyX0KWuvE82aIvr7n73Wk/sm9xjjKz0WY2mOwhkLtX6SdNzJ1bPFsxqzv38ZLnJ7b3dT0eb0J7M8ecqvEGOh1zFG/6gGY8p3HnfzxLv+zNdI7TUZlznC6PN7qoEilgZkPJHoh4prt3OCl1963uPpVsBcgjzWxK8v4PASvcfUYndvcudz+MbMLx6WZ2dNLeSnZL/3J3fwvZHMOaT4g3swHAh8keR5C2VVnO205J+7n7k8C3gLvJ5unNYtsyvCLSherFG2jemFMUb0J73ZijeCPSs3bmc5zuiDe6qJK+pDNL3XeamfUnCzY3uvtPi/qGW9T3AscnTe8EPmxmi8hu1b/HzG6osY0l4c8VZIsGHJl0WQwsjr4pupUsANXyAWCmuy+v0haW8/aX3H0zVJbzrjauq939cHc/GniVLPdaZGfXsHgDTRlziuINdDLmKN6I1KRznLzS5zhdHW90USV9ySPAvma2Z/gG4yTyDwjsNDMzsrzeJ9390hp9xprZiFBuA44lWXnI3b/s7ru5++Qwnt+4e4c7QmY2xMyGtZfJHsQ8N9nWMuAFM9s/vPRe4ImCwziZGqk4VJbztsHhWCvLeVcZW1iG1/YgyzeutsKcyM6mR+NN6NfMMaco3kAnY47ijUhNOsfJK32O09Xxplse/ivSCO6+xczOIFuqtgW4xt3npf3M7CayJV/HmNli4Bx3vzrp9k6yZ6PNCfnEAF9x92lRnwnA9WbWQvYFxS3uXnM50TrGAbdln31agR+5+/9W6fcZ4MYQUJ8FTqu2sRC0jgU+Va3d3aebWfty3lvIluat9UTxn5jZaGAzcHonJo6K9HkNiDfQpDGnXryB7Yo5ijciVegcZ5suPMfp0nhj7h0W8hAREREREZFOUvqfiIiIiIhICbqoEhERERERKUEXVSIiIiIiIiXookpERERERKQEXVSJiIiIiIiUoIsqERERERGREnRRJSIiIiIiUoIuqkRERERERErQRZWIiIiIiEgJuqgSEREREREpQRdVIiIiIiIiJeiiSkREREREpARdVImIiIiIiJSgiyoREREREZESWhs9AJHtNWbMGJ88eXKjhyGRGTNmrHT3sY0eh0hXa4Z48/jjsGVL7fbWVjj00J4bTzNQzJG+qhlijuR1Nt7ookp6ncmTJ/Poo482ehgSMbPnGj0Gke7QDPHGrLh9yxbY2UKiYo70Vc0QcySvs/FG6X8iIiIiIiIl6KJKRERERESkBF1UiTTa+PFZfk+1n/HjGz06EREREamjoXOqNBmv+SxatIiVK1fWyeCXLrV8+Y61yXZp9nizcePGXH3gwIFdst3169fn6m1tbV2y3a6iBQekr2r2mJNauXJlpbylaGUUoF+/bd/JDxgwINc2YsSIrh1YF9I5jnSnhl5UaTJe8zniiCMaPQSRbtFs8Wbr1q25+qJFi3L1vffee4e21dLSkmubM2dOrj5lypRK2eqtgNADtOCA9FXNFnPqufLKKyvlVatW5drSi6yhQ4dWyrvttluu7cQTT+yG0XUNneN0nfHji7/3HTcOli3rufE0A6X/iYiIiIhIp9VLpNkZE220pLqISANs3rw5V3/hhRdy9aI7Ve6eq6d3p2Ivvvhirn7wwQd3dogi0iDxZ7zojnIaC9I7Sv3796+U07vjra35U8A45bjeXey4PU0xPv7443P1X/ziFzW3E483HY9Ib6M7VSIiIiIiIiXookpERERERKQE3WsVEWmAQYMG5epXXXVVrh6voDV16tRcW1Fqzs9//vNc/bvf/W6uftxxx23XOEWk5xWl/73xxhuVcrwKH+TT/VJnnHFGrp6uMDphwoRKOV3Rb8OGDbn6pk2bKuVhw4bl2mbNmlVzDKk45S9NTyxKaxZpRrpTJSIiIiIiUoIuqkRERERERErQRZWIiIiIiEgJmlMlItIA6ZLqDzzwQK7+yCOPVMqHHHJIru20007L1c8///xKOZ37ED/sV0R6h3iuVBoriuZNTZs2LVf/9re/XSkvXLgw1zZq1KhcPZ67NXHixFxb+miGeP5TOucrXeY9nqv1hS98Idd25plnVsqaQyW9ne5UiYiIiIiIlKCLKhERERERkRJ0USUiIiIiIlKC5lSJiDRAOi9i/PjxufqWLVsq5fnz5+faTj/99Fw9fubVyJEjc21jx44tNU4R6Xnxs6iK5lCdfPLJufott9ySqw8dOrRSHjx4cK4tnQu1Zs2aSnnp0qWF41u/fn2l3NbWlmtLnze1cePGSvmrX/1qru3iiy+ulC+77LJc20c+8pFcPY6J8fOtRJqF7lSJiIiIiIiUoIsqERERERGREnT/VESkCaQpNEuWLKmUhw0blmsbMWJErj5w4MBKOV1SfciQIV01RBFpAvfee2+l/LOf/SzXNmnSpFw9Xo49Tp+rZtOmTZXyokWLcm0HHXRQrh6n9K1atSrXFqcjp/U0HsXj+8QnPpFrmzp1aq6+zz77VMrpsu1pKqNII+hOlYiIiIiISAm6qBIRERERESlBF1UiIiIiIiIlaE6ViEgTSOcsLFy4sFIuWlI5bU/nVE2cOLHm+zQvQaQ59etX+zvvK664olJuaWnJtaXzpuLlzdPPe7xsO+TjSBpzXnzxxVw9nsdZL47E7en44v2kx3zWWWfl6nfccUfNfYg0A92pEhERERERKUEXVSIiIiIiIiUo/U9EpIfEaTBp+kq61HBr67bwXC+9Zty4cZXyyy+/XHOfItL7pJ/h3/3ud5Xy4MGDc23xEuWQjxXpdtK+cUpfmlaYpgquXbu2Uk4fB5HupygGxemAw4cPz7Xdf//9ufqcOXMq5YMPPrjmNkUaRXeqREREREREStBFlYiIiIiISAm6qBIRERERESlBc6pERHpI0TLACxYsyNWLllTeuHFjrr569epKefTo0bm25557bofGIyLN4ca7YMIAAB/QSURBVOabb87VX3nllUo5nYeUzn2KP+O77LJLrm3dunW5ejzHKl6KHTrO+Yz3k8ajQYMG1RxD0fyqenOxLrnkkkr5uuuuq7kdkUbRnSoREREREZESdFElIiIiIiJSgi6qREREREREStCcqgLf//73c/W5c+fWbCtS7xkzIiL33ntvrr7HHntUyv3798+1pfMmYml8mT9/fheMTkQa5fe//32uHj9DKn3WVGrAgAGV8vr163Nt6XvjOBM/PwpgxIgRNfeRnuOk87Hi+aFF50PpPtNnZT3wwAM1xyDSDHSnSkREREREpARdVImIiIiIiJTQVOl/8a3ptra2TveNb2/Xk95Ojt155525+osvvpir77rrrpXyqaeemmv7xje+kavvvvvulXK9dL/0VnmsaLwi0ns988wzufrYsWNz9YEDB9Z8b7o0chxj0nizdOnSHR2iiDSBmTNn5upF6XTp+VAcDzZs2JBrS5c+j9Pv6sWRuL3eOdimTZtq9o33mR5LGgMHDx5cuB+RRtOdKhERERERkRJ0USUiIiIiIlKCLqpERERERERKaKo5VfE8pTPOOCPX9u53vztXrzfnakeky6QfeeSRuXqcC7zbbrvl2m6++eZcPZ5/deKJJ+bahg0blqvH86bS+VVpjnFnadl2keaWzpOI5x1A/jNctPQx5OdKxPMtABYvXlxqnCLSWAsXLszV43OG9BwhfdxCHA9aW/OnfOkS5vG20r5pXIljUrqdVPzeor7p+U86hjVr1hTuR6TRdKdKRERERESkBF1UiYiIiIiIlKCLKhERERERkRIaOqfqjTfeYO3atZV6nPt/++235/quW7cuV58yZUqlPGrUqFxb/CyDNL/4+eefz9WvvfbaSnn8+PG5tjFjxuTqd9xxR6V8wgkn5NpWrVqVq0+bNq1Snj9/fq5tr732ytWPPfbYSnnSpEnsqDgfuSivGvT8K5FGmz59eq6efkbjz3M6byLtG7en8yknTJiQqy9YsKBS3meffbZjxCLSCMuXL8/V43OTdL5l0ZylNG6ksSKup9tJzynivul2077xHNB0vNsz/3vRokWV8uuvv55rGz58eKe3I9JddKdKRERERESkBF1UiYiIiIiIlNDQ9L/169czd+7cqm1xWiDAjTfemKsfcsghlXK81Hlaj1NdAObMmZOrx8sYH3XUUbm2dMnj4447rlKOUwyrjeH444+vlFesWJFre/rpp3P1P/zhD5XygQcemGt785vfXCkfccQRubaxY8fm6nFKn9L7RJrbvHnzcvU0hSaOKelSwmnKTJyqk7al6TYvv/xypaz0P5Hml6b/xv+/Fz2KAfJpxEXpfqk0hS+dDhFPyUinZ6TnH+m2ao2vqF/qqaeeytXf+ta3dvq9It1Fd6pERERERERK0EWViIiIiIhICbqoEhERERERKaGhc6q2bt2aW4r8lVdeqZRbW/NDe+2113L12267rVIeOXJkri2eQzBs2LBc2zve8Y5cfb/99quU0zkN8bLtACtXrqyU0xzidFn3+FjS+Vd77LFHzXq6TOgDDzxQKT/yyCO5tnS7I0aMqJTTpdl33XXXXP2AAw6olAcOHIiI9Kx4eWDoOC8znieVLm+cxsd0XkWt7QA888wzlfLb3va2To1VRHrWkiVLarbFc6HS+VZdJd1uOt8pjivpvM30XKpI/N40BhYd2x//+MdcXXOqpBnoTpWIiIiIiEgJuqgSEREREREpQRdVIiIiIiIiJTR0TlW/fv0YMmRIpR4/v+m0007L9Z08eXKuHs9Z2rBhQ64tnls0aNCgXFvad/bs2TXHN3To0Fw9nsOUzmFYtmxZrh7nBg8fPjzXlr43nkeVPgsinasVS48lfh7Wiy++WHPsABdccEGlfMopp1TK8Rw3Eek+zz//fK6+//775+rpPIVY+nyZeI5VOvchnd+QPqtPRJpP+hymWup93ndU+qyp+Pl2aXt6npWOKa6n44vnZqVzqoqeW7V06dKabSKNojtVIiIiIiIiJeiiSkREREREpISGpv+tWrWK22+/vVKfMGFCpZymr6VLje+1116VcrpEeXw7Od3Oxo0bc/WtW7cWji8WL+vev3//XFu6ZHlR+l8qTs0bN25czfGlaYPpLfe4nv6+0t9DnD506aWXVsrLly8vHKuI7Lj485ym76ZpMUXLpKdpMfHnOY1xaRpPmqosIs3n2Wef7VS/9PEK6TLkcWxIY0xR31T66JU4lqXxaHvGENfT/Rel/7300ks120QaRXeqREREREREStBFlYiIiIiISAm6qBIRERERESmhoXOqNm7cyIIFCyr1vffeu1KeMmVKru/cuXNz9cWLF1fKRcuHF+Xkpu3pHIa0Huf7pvnFaX5v3N7W1pZrS+djxVauXFlzfKtXr861pXO+4vZ0Ofh0/sYzzzxTdTtF8zhEpJznnnuuZlv62IO1a9dWymnMKJrDkM5nSOdepku5i0jziR+RUiSdh5TOE0/nVHZWGkeKYk46hqIxpedO8Zyq9DESRedK8WN1RJqF7lSJiIiIiIiUoIsqERERERGREnRRJSIiIiIiUkJD51T169cvN4/goYceqpTrPZMpbl+3bl2uLX4u1JgxY3Jta9asydWLnlOV5iLHz4NInw2RPnshfk5VKs0Tjuc/pXnU8bGlz55K50nF+cjp+OJnd6XvPe+88yrlc845p+a4RaSc+fPn12xLY0j8eU7jSdo3jmPpXIg0FixZsqRzgxWRhlm4cGHNtvjzn56nrF+/PlcvmpdUJJ1D9aY3vSlXf/nllyvl9FwknVMVx6D03G7kyJFVtwkdxx5vR8+pkmakO1UiIiIiIiIl6KJKRERERESkhIam/+2xxx5cdtlluXq7UaNG5fqmS43Ht5DT1Jg4ZS5ddnPYsGG5epwWl6bUpGkz8e3w9BZ7ers7HlN6u7toP0Vt6e9kxIgRuXqcIpn23X///XP1Y489lmq+973vVX1dRMrbntS7NGU3VrS8cboscRqb0kcziEjzic950nOR+DOexoK0b5oOXNQW19NzkaVLl+bqRY+rKTqPee2113JtxxxzTKV811135drSGBinA6apgiLNQHeqREREREREStBFlYiIiIiISAm6qBIRERERESmhoXOqWlpacstpfvOb32zgaEREulc8n6longTk5yykcwvS+QzpvKlYOr+haK6WiDSHOFakS4vH88YnTZqUa0vnjU+fPr1SnjhxYq5t48aNuXpRHClqS6XxKY5B6WNtYvH5IHScNxXHzKLH4Yg0iu5UiYiIiIiIlKCLKhERERERkRIamv4nIrIziZdUT1N60pSZOL2lXupNnF6Tpvul241TftKUw3RMItIYcfpfW1tbri1+VMzUqVNzbWla8UMPPVQpp0uoF8WVtG9R2nC6naJ6ut045W+//fbLtf3qV7/K1ceMGVMpFy3pLtIoulMlIiIiIiJSgi6qREREREREStBFlYiIiIiISAmaUyUi0kNef/31SnngwIG5tnSuQaylpaWwbzxnIZ1TldZj8dwMgHHjxtXsKyI9J55TWTTX8ZhjjsnV582bV7NvUSxIpfOi0qXa42XdyzymYfTo0ZVyPGcKOs6pisdfFC9FGkV3qkRERERERErQRZWIiIiIiEgJuqgSEREREREpQXOqRER6yJo1ayrl7XkmVDp/IK3Hc67qbTd+NtWqVatybZpTJdIc4jmX8fyq1AknnJCrz5o1q2bf9Ll06bOe4jlLaVsaczZt2lT1fdX6xs/GSw0YMKBSPvroo3NtF154Ya4ez/MaPnx4zW2KNIruVImIiIiIiJSgiyoREREREZESlP4nItJDNmzYUCkPGTIk15am+MT1NBUnXcI4ThVKl1+P03QA9txzz6rjEZHmEafFpYYOHVopp8uQr127NlePU+bSOJLWi6xevTpXj1P+0tiVLsee1mNxGl/6mIk0zsXjLUqJFGkU3akSEREREREpQRdVIiIiIiIiJeiiSkREREREpATNqRIR6SEPPvhgpTxs2LDCvm1tbVXL0HG+RbyMejp/IV3eOJ5H9dRTT+XaDj300MIxiUjPiOdcxo9igOK5kOkjFeJ5SunS52k9no+ZxpF0/lXcnvZNt9vauu1Uc9CgQbm2119/vWq5mvhYRo8eXdhXpBF0p0pERERERKQEXVSJiIiIiIiUoIsqERERERGREjSnSkSkh3z605+ulC+88MJcW/o8qfi5MEuXLs21jRo1KlffvHlzpZzOt0rnbq1bt65SHjlyZGeGLSI9bNq0aZXyypUrc23r16+v+b4FCxZ0eh9Fz8ZL52LG86IgP48qfTZe+nypdFux2bNnV8pf+9rXOv0+kWakO1UiIiIiIiIl6KJKRERERESkBKX/iYj0kPPPP79SPvjgg3NtTzzxRK4ep/jst99+ubapU6fm6nFK3+DBg3Nt6bLpJ5988naMWEQabcyYMZ3um6b/xkuYp8utp/U4jThNvUuXQi9KFUzFfdN05AMOOKDwvSK9ie5UiYiIiIiIlKCLKhERERERkRKU/iciIiIi0sd96UtfKmy/6KKLemgkfVNDL6pmzJix0syea+QYpINJjR6ASHdQvMl89KMfbfQQUoo50ic1W8xZtWpVo4fQwQUXXFC13I0Ub6TbNPSiyt3HNnL/IrLzULwRkZ6kmCOyc9GcKhERERERkRI0p0qkDxk/HpYvr942bhwsW1acU618ahEREZHtp4sqkT6k1gVVvTYRERGRRugrX/ZavYe2iTQbM3sJKJr8OwZY2YlNdaZfT2+rt459kuYPSF/UiXgDzfd5bMT+unJbnemjmCN9UhOe4/T1WNKZfp2LN+7ecz/wVYd5DrMdZjm8rYu2e5/DETvUB97jMNNhrsP1Dq3h9S+EMc4KbVsdRjmMdfhdeO3Po+383OFNBfs/0+HUUH67w/Sw7Scdzu2G3/UihzEl3v9jh3179N9HF/0Aj3ZVv57eVm8ee2/5Ad8KPgt8Hvjj4P8M3q+H9n0A+B/AN4L/S9J2PPhT4AvAvxS9vif49PD6zeADwuufAZ8LPi167V3g3ynYfxv4b8FbwCeDrwd/DPxJ8IfBP97Nx/9t8Pc0+t9A1x9X830ee3Ms6UvxpnO/Nx8P/mPwheAzwmd6vx3Yzgjwfypo/1yIGfPAz4xePxd8SYiLs8D/NLz+TvDZ4I+C7xvt4+6imAl+K/heoTwU/Iro2O4D36FzP/CvROUB4PeDtzb676/n/7005+exGbfVlfvrzE/PLVRh9g7gQ8BhuB8CvA94ocf2X31M/YDrgZNwn0L2zcDfAuB+Me5TcZ8KfBn4Le6vACcD/wUcCZwZtvNnwGO4v1hjP63AJ4AfhVeuBz4Ztj0FuKU7Dm+HmbUAlwNfbPRQRLrBenemuvNm4FjgA8A5aSezbkmPfgX4LPDtZF8twH+GsRwEnGzGQaH5W8B33NkHeBX4u/D6x4BDgN8Dx5lhwNeArxfs/xPAT93ZGuoL3XmLOwcCJwFnmnFa+qYu/F1cBhQ/KEVkJxI+t7cB97mztzuHk51zjNuBzY0A/qnGfqYA/0B27nIo8CEz9om6fCfExanuTAuv/TPwp2TnOp8Or50NfNOdN2rs581AizvPhpeuIot7+4ZjO43srsCO+Ep7wZ1NwK+Bv97BbYl0uZ5c/W8CsBL3jQC4r6xchJj9G2aPYDYXsx9gZuH1+zD7FmYPY/Y0ZkeF19sw+zFmT2J2G9BW2YvZ5Zg9itk8zM6rM6bRwCbcnw71e4C/rNLvZOCmUN4MDAYGAlvDBdOZwL8X7Oc9wEzct4T6rsDS8HvYivsTYeznYnZNOO5nMftsdFynhN/DLMyuCBc+9Y83+139ArN/qLOdNZhdgtnjwDuAB4D3heMT6ZPcWQF8EjjDDDPj42bcbsZvyP7DxowvmPGIGbPNOC+8NsSMu8x43Iy5Ztl/7GZcZMYToe+3q+3PnUfI4kjsSGCBO8+Gk4UfAyeEE673ALeGftcDfx7KBvQni0ebgVOAX7jzSsEhfwz4eY3fxbPA58ku+jDjXDN+aMaDwA/NaDHj4uh38anQb4IZ95sxK/wujgp9rwv1OWacFfbxHDDajPEFYxTZmRwDbHbnv9pfcOdxdx4IMeni6HPUHmeGmvFrM2aG108Ib70I2Dt8Fi9O9nMgMN2dde5sAX4L/EWdsbWf7wwGNpuxN7C7O/cVvKcSY0L/twFnt1+EufNHd+4K7Z8PxzbXLHxJnb3+MzNmmDHPjE+G1y4C2sKx3Ri6/izsT6Q59NgtSxga0t2edvi+w7ujtlFR+YcOfxbK9zlcEsp/6vCrUP68wzWhfIjDlkpqX/u2oCW8/5BoW0ckYzKH56L3ftdhTtJnsMMr0XZ3cbjL4VGH9zp81uHjdY79PIfPRPV/c3jV4TaHTzkMCq+f6/B7h4EOYxxedujvcKDDHQ79Q7/vR6mEtY53kcNkh19FfYu24w5/lYz7HofDe+zfSBf9AJ/sqn49va3ePPbe8gO+psprq8DHgX8cfDH4qPD6+8F/AG7g/cDvBD8a/C/Br4zevwv46JC+F+aq+oiCMZwbp/+BfwT8qqj+N+D/AT4GfEH0+u7gc6M+j4HfAD4M/Dfg/Qv2OQB8WVSf3L6t6LUR4OujMc4Abwv1T4KfHcoDQ0rQnmTpk18Nr7eEsRwOfk+83ah8JfhfNvrfQdf+m2q+z2NvjiV9Kd7UP1b/bK2U3RBn7gmfq3Hgz4NPAG8FHx76jCFLDbZqn+loWweCPx3i1GCyNOTLQtu54ItCqt814CPD61PBHwK/F3w3shTFwmkBZOnFB4fyh8Fvq9HvcPA54ENCiuA88LeEtvb420aWrjg61Nck22gBf6nRf4c9/2+mOT+PzbitrtxfZ3567k6V+xrgcLJvhV8Cbsbs46H1GMymYzaH7FvZN0fv/Gn4cwYwOZSPBm4I250NzI76/xVmM4HHwnYOopbsN3kS8B3MHgZWQyUtpt2fAQ+Spf6B+2u4fxD3I4CZof1WzK7E7NaQ5piaEI65fb/nA0cAdwMfBf436nsX7htxXwmsIEsBeC/Z7+4RzGaF+l6dON6fA9fi/t+hXrSdrcBPknGvAN5U5Xiamrv/oKv69fS2evPY+5B7fNvdnveHn8fIPu8HAPsCc4BjzfiWGUe58xrwGrABuNqMvwDWdecg3fmhZ6l7pwBnAd8DPmDGrWZ8x6xDfB8DrKqzWUvqt7uzPpTfD5xqxixgOtmd/n2BR4DTzDgXONid1cCzwF5mXGbG8cDr0TZ7ZVwp0oyfx94cS3ayeFPkXcBN7mx1ZznZ3aW3kn1Ov2nGbOBXwETqpAu68yRZKvHdZOccs9h2vnM5sDcwlSyL5pLwnlnuvN2dY8jOFZYCZsbNZtxgVnWf+fOd4mO7zZ217qwhO9c7KrR91ozHgYeA3cniTLVj2gpsMmNYJ/bXZzTr57EZt9WV++uMnk3tct8K3AfcFy6g/hazHwPfB47A/QXMzgUGRe/aGP7cSr3xmu0J/AvwVtxfxey6ZFvVxvQH2j/IZu8H9kt6nMS21L/U14BvkKUH/o4sReenwHFJv/UdxuG+ELgcsyuBlzAbHVo2Rr3aj9mA63H/cm4b9Y/3QeB4zH4ULiCrbyezIfz9xAaFsYv0WWbsRfZZWxFeWhs3Axe6c0WV9x1GNt/gAjN+7c75ZhxJ9mXFR4AzyL4k6owlZCcP7XYLr70MjDCj1bOUnfbX43G8CTgy7P+3YZ9nh3HcE3XtGIc6egvwZFRPfxefceeX6ZvMOBr4IHCdGZe6899mHEoWCz8N/BXZfC5QXBGJzSOLF9vjY8BY4HB3NpuxiPqfbdy5GrgawIxvAovD65UHbphxJXBn/L6Qhnw22fnQZWTzrSeTpQp/NdlNHGfmAYea0eLe4Qvrqsz4E7I59+9wZ50Z99U5toFkX2aJNFxPLlSxP2bxtw1TyRaGaP+wrMRsKJ0LLveT3eEBsylkk7UBhpOdBLyG2TiySd/1xrVr+HMg8K+wLa8Zs12Ad1NtDkJ2LLvhfh9ZvvEbgBPP79rmSYgmhJp9sDJvLPsGZivF3yD/GvhINNZRmE2i/vH+G9nE9v+ss51a9gPmFrSL9GpmjCX7zP+HO16lyy+BT5gxNPSfaMau4UJmnTs3ABcDh4U+u3g2yfssssngnfUIsK8Ze5oxgOzk5fYwpnvZFhf/lo7x6Otkn3XI4o+TxaPBcSd3XgVazKqfoJgxmWwBjctqjPGXwD+a0T/03y/MLZsELHfnSrJJ6YeZMQbo585PyE7GDou2o7giss1vgIHtc4cAzDjEjKPI5jb/dZijOJYsS+dhYBdgRbigOgZo/398NdS+a2PGruHPPcjmU/0o1CdE3U6k4+fzVGBauIPffr7TIcYElfMddxYCjwLnhQszzJhsxgfDsf25GYPNGBL2+0A4tlfDBdUBwNujbW9ujz9hW6OBle4d5qiKNERPLlQxFLgesycwm02WpnYu7quAK8k+xL8kO7mo53JgKGZPAueTpQaC++NkaTrzyYLFg53Y1hfCdmYDd+D+m6jtROBu3NdWed832PYNzU3AP4axf7dK31+QBcN2fwM8FVLwfgh8rMpdom2yhSzOBu4Ov7t7gAmdPN7PAW2Y/XvN7VSTXaStx31ZzXE1ITM73syeMrMFZlZ1lTEzu8bMVphZzRM7M9vdzO41syfMbJ6Zfa5Kn0Fm9rCZPR761FwYxcxazOwxM7uzoM8iM5tjZrPM7NEafUaY2a1mNt/MnrQq6aZmtn/YRvvP62Z2ZpV+Z4VxzzWzm8ysxsm2fS70mVdtO71Q+2TneWSpM3cDVf/u3Lmb7LP1BzPmkN2NHgYcDDwcUuHOAS4Ir98ZUnJ+R7boQ44Z481YHNrONmOxGcPDXagzyGLgk8At7swLb/tX4PNmLCBLubs62t5bwjhnhpd+RJaa+E7yacXt7iZLu2m3txmPmWX7BL7nzrU1fm9XAU8AM82YC1xBdif9T4DHzXiMbCWu75KlI90Xfj83kK1mRjgh2ofsRKvX68l4E/o1XczpbLwJfevGnD4YbwqFL05OBN5nxsIQly4ElpGtCjgbeJzs4uuL7iwDbgSOCDHpVLJzANx5GXgwLPyQLlQB8BMzngDuAE53r3yZ++9hwYvZZAtnnNX+BjMGAx9n25ezlwLTgP9P/CX0NneRxYR2f0+WmrggxI3ryC4IZ4byw2TpxFe58xhZ3GoNMekishTAdj8AZkcLVRwT9rfT0DlO157jdHm86YqJWfrpxE+2KEXvee4TnOXwdw0fx3b8AC3AQrLc7wFk/xEdVKXf0WTfnFed0Bv6TAAOC+VhwNPptsjSoYaGcn+y/xjeXmN7nyc74b2zYJ+LqPNsMbLV3/4+lAcANRdDiH4ny8geXBe/PhH4IxAWIeAWqiy4Qrbk/1yybyRbyS5C9mn037V+duwH/DDwHzZw/yeCf73Rv4euOZaejTehraljTq14E9rqxhzFm97/ExaXeAi8pQf29VN24HlevfVH5zg1fyc7dI7THfGmJ+9U7ey+RK27Qs1pFdk/7t4kLEvtz7p7ZVnqtJO73w+Fy07j7kvdfWYorya7ezAx6eOeLcACWcDpDx1TyMxsN7L5Jldt9xHlt7MLWbC8Oux/k2d3eou8F1jo7tWezt4KtFm2bP5goNpz1sIyvL7Os0cCdGYZXmlSnn07fK9lz8VqhFbCJPg+oEfjTWhr9phTFG+gfsxRvOnlPFvY5hyq/PvtSiFN+mfuPF23c9+hc5yOypzjdHm80UVVT3F/iuwfeu/gfi3bnqvVW0wk/0DpxXRBYDezyWQT+KdXaWuxLI1zBXCPu3foQ5Ym8UWo/rDEiAN3m9kMM/tklfY9yVZVujbcZr/KzIbU2WbVhVbcfQnZ/JnnyVZ0es3d767y/rnAUWY22swGky3MsHuVftJLuHONd3LSeDfs+3/c665A2Fv0eLwJ7c0cc2ou7NTJmKN40we480t3nu/mfWxy57/r9+xTdI7TUZlznC6PN7qoEilg2eIpPwHOdPfX03Z33+ruU8lWZTvSsoVT4vd/CFjh7jM6sbt3ufthZAuOnG5mRyftrWS39C9397eQLVJSNac67HsA8GHgf6q0jST7hmtPsuWth5jZKVWOr2gZXhHpQvXiDTRvzCmKN6G9bsxRvBHpWTvzOU53xBtdVElfUmtZ6h1iZv3Jgs2N7v7Tor7hFvW9wPFJ0zuBD5vZIrJb9e8xsxtqbGNJ+HMF2QTlI5Mui4HF0TdFt5JfVS31AWCmuy+v0vY+4I/u/pK7byZ7FMD/qzGuq939cHc/mmw1yZ0p3UKklobFG2jKmFMUb6CTMUfxRqQmnePklT7H6ep4o4sq6UvCstS2Z/gG4yTg9h3ZkJkZWV7vk+5+aY0+Y81sRCi3AccSVmFq5+5fdvfd3H1yGM9v3L3DHSEzG2Jmw9rLZA9anZtsaxnwgpntH156L9lqbLWcTO1nrD0PvN3MBodjfS/55xPFYwvL8FpuGV6RnVyPxpvQr5ljTlG8gU7GHMUbkZp0jpNX+hynq+NNzz78V6QbufsWM2tflroFuMbd56X9zOwmsiVfx5jZYuAcd7866fZOsqXv54R8YoCvuPu0qM8E4HozayH7guIWd6+5nGgd44Dbss8+rcCP3L3aktifAW4MAfVZ4LRqGwtB61jgU9Xa3X26md0KzAS2kC3NX+uJ4j+x7OHUm4HTOzFxVKTPa0C8gSaNOfXiDWxXzFG8EalC5zjbdOE5TpfGG3Ov9rxLERERERER6Qyl/4mIiIiIiJSgiyoREREREZESdFElIiIiIiJSgi6qREREREREStBFlYiIiIiISAm6qBIRERERESlBF1UiIiIiIiIl/B9XnTZjPr3dowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first X test images, their predicted labels, and the true labels.\n",
    "# Color correct predictions in blue and incorrect predictions in red.\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions[i], test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions[i], test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "R32zteKHCaXT"
   },
   "source": [
    "## Use the trained model\n",
    "\n",
    "Finally, use the trained model to make a prediction about a single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yRJ7JU7JCaXT"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Grab an image from the test dataset.\n",
    "img = test_images[1]\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vz3bVp21CaXV"
   },
   "source": [
    "`tf.keras` models are optimized to make predictions on a *batch*, or collection, of examples at once. Accordingly, even though you're using a single image, you need to add it to a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lDFh5yF_CaXW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Add the image to a batch where it's the only member.\n",
    "img = (np.expand_dims(img,0))\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EQ5wLTkcCaXY"
   },
   "source": [
    "Now predict the correct label for this image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "o_rzNSdrCaXY"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.0441209e-04 2.0598597e-13 9.9973267e-01 1.3913811e-08 1.3902804e-04\n",
      "  1.4598676e-14 2.3888417e-05 7.5015997e-17 3.2095410e-11 1.2332260e-13]]\n"
     ]
    }
   ],
   "source": [
    "predictions_single = probability_model.predict(img)\n",
    "\n",
    "print(predictions_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6Ai-cpLjO-3A"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_value_array(1, predictions_single[0], test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cU1Y2OAMCaXb"
   },
   "source": [
    "`keras.Model.predict` returns a list of lists—one list for each image in the batch of data. Grab the predictions for our (only) image in the batch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2tRmdq_8CaXb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions_single[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YFc2HbEVCaXd"
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